#typescript #graph #json #jsonvisio #nextjs #react #tool #visualization

https://github.com/AykutSarac/jsonvisio.com

#go #graph #graph_algorithms #graph_theory #graph_traversal #graphs

https://github.com/dominikbraun/graph

#rust #backend_as_a_service #cloud_database #collaborative #database #database_as_a_service #developer_tools #devtools #distributed #distributed_database #document_database #graph_database #hacktoberfest #iot_database #nosql #realtime_database #serverless #sql #surreal #surrealdb #web

https://github.com/surrealdb/surrealdb

#go #acl #authorization #authzed #ciam #cloud_native #database #distributed #distributed_systems #fine_grained_access_control #graph_database #grpc #kubernetes #latency #permissions #production #scale #security #security_tools #spicedb #zanzibar

https://github.com/authzed/spicedb

#rust #client_server #cozo #cozoscript #cross_platform #database #datalog #embedded_database #graph #graph_algorithms #graph_database #graphdb #relational_database #single_executable

https://github.com/cozodb/cozo

#c_lang #age_database #agensgraph #analytics #graph_database #graphdb #multi_model_dbms #postgresql #postgresql_extension

https://github.com/apache/age

#rust #analytics #big_data #data_science #graph #graph_analytics #graph_computation #graph_computing #graph_data #graph_neural_networks #gremlin

https://github.com/alibaba/GraphScope

#go #cli #dependencies #dependency #dependency_graph #golang #graph #linter #terminal #tree #typescript

https://github.com/gabotechs/dep-tree

#javascript #graph #jsmind #mind_map #mind_mapping #mindmap #mindmapping #svg #xmind

This tool is called "Simple Mind Map" and it helps you create and manage mind maps easily. You can use it to develop web-based mind map products without relying on any specific framework. It supports various types of diagrams, such as logical structure graphs, mind maps, organizational charts, and more. You can customize the appearance with multiple themes and styles, drag-and-drop nodes, export to different formats like JSON, PNG, SVG, PDF, Markdown, and XMind.

The tool also offers features like keyboard shortcuts, search and replace functions, mini maps, watermarks, and collaborative editing. It's available online or can be downloaded for Windows, Mac, and Linux. The project is open-source under the MIT license and has a community support group for any questions or custom development needs.

Using this tool simplifies your workflow by providing a flexible and powerful way to visualize ideas and projects.

https://github.com/wanglin2/mind-map

#typescript #bpmn #diagram #flow #flowchart #graph #mvvm #visualization

LogicFlow is a powerful tool for creating and editing flowcharts. It offers a user-friendly interface to visualize, create, and manage complex logic flowcharts easily. You can customize nodes, connectors, and styles to fit your specific needs. Additionally, it supports various plugins and allows data conversion with other backend engines like BPMN and Turbo. This flexibility makes it easy to integrate into your business systems without requiring extensive coding knowledge.

The benefit to you is that you can quickly and efficiently create customized flowcharts that meet your business requirements, all while having the option to extend its capabilities with custom plugins and integrations.

https://github.com/didi/LogicFlow

#typescript #antv #dag #diagram #diagramming #erd #erdiagram #flowchart #graph #graph_editor #javascript #javascript_diagramming_library #svg #typescript #uml_diagram

X6 is a powerful graph editing engine from AntV that makes it easy to create flowcharts, DAG graphs, and ER diagrams. It offers simple and customizable node styles using SVG, HTML, React, Vue, or Angular, and comes with over 10 built-in extensions like box selection and mini maps. X6 is data-driven and event-driven, allowing you to focus on your data and business logic. It supports modern browsers and server-side rendering, making it versatile for various projects. This tool helps you build complex graphs quickly and efficiently, saving time and effort in your development process.

https://github.com/antvis/X6

#python #deep_learning #graph_neural_networks

DGL (Deep Graph Library) is a powerful and easy-to-use Python package for deep learning on graphs. It allows you to work with graphs on both CPU and GPU, making it highly scalable and efficient, even for large-scale graphs. DGL is compatible with major frameworks like PyTorch, Apache MXNet, and TensorFlow, giving you flexibility in your projects.

The benefits include DGL optimizes communication, memory consumption, and synchronization, allowing it to handle billion-sized graphs efficiently.
- **Ease of Use** DGL offers a variety of functions for computing with graph objects and includes state-of-the-art GNN models and modules.
- **Community Support**: Active community channels like Slack, forums, and monthly seminars help you stay connected and get support when needed.

Overall, DGL simplifies the process of working with graph neural networks, making it a valuable tool for researchers and practitioners alike.

https://github.com/dmlc/dgl

#cplusplus #android #audio_processing #c_plus_plus #calculator #computer_vision #deep_learning #framework #graph_based #graph_framework #inference #machine_learning #mediapipe #mobile_development #perception #pipeline_framework #stream_processing #video_processing

MediaPipe is a tool that helps you add smart machine learning features to your apps and devices. It works on mobile, web, desktop, and other devices. You can use pre-made solutions for tasks like vision, text, and audio processing, or customize the models to fit your needs. MediaPipe also offers tools like Model Maker and Studio to help you create and test your solutions easily. This makes it easier to delight your customers with innovative features without needing deep machine learning expertise.

https://github.com/google-ai-edge/mediapipe

#python #agent #agents #ai_search #chatbot #chatgpt #data_pipelines #deep_learning #document_parser #document_understanding #genai #graph #graphrag #llm #nlp #pdf_to_text #preprocessing #rag #retrieval_augmented_generation #table_structure_recognition #text2sql

RAGFlow is an open-source tool that helps businesses answer questions accurately using large language models and deep document understanding. It extracts information from various complex data formats, such as Word documents, Excel files, and web pages, and provides grounded citations to support its answers. You can try a demo online or set it up on your own server using Docker. The setup is relatively straightforward, requiring a few steps like cloning the repository, building the Docker image, and configuring the system settings. RAGFlow offers key features like template-based chunking, reduced hallucinations, and compatibility with multiple data sources, making it a powerful tool for truthful question-answering capabilities. This benefits users by providing reliable and explainable answers, streamlining their workflow, and supporting integration with their business systems.

https://github.com/infiniflow/ragflow

#clojure #clojure #clojurescript #git #graph #knowledge_base #knowledge_graph #local_first #markdown #note_taking #org_mode #pkm

Logseq is a tool that helps you manage your knowledge and collaborate with others while keeping your data private. It offers powerful tools for organizing notes, collaborating, annotating PDFs, and managing tasks. You can use it to visually group and link your notes, videos, and images on a canvas. Logseq supports multiple file formats like Markdown and Org-mode and has a growing ecosystem of plugins and themes to customize your experience. It also has mobile apps, making it accessible anywhere. By using Logseq, you can improve your productivity and streamline your workflow easily.

https://github.com/logseq/logseq

#cplusplus #caffe #convolution #deep_learning #deep_neural_networks #diy #graph_algorithms #inference #inference_engine #maxpooling #ncnn #pnnx #pytorch #relu #resnet #sigmoid #yolo #yolov5

This course, "_动手自制大模型推理框架_" (Handcrafting Large Model Inference Framework), is a valuable resource for those interested in deep learning and model inference. It teaches you how to build a modern C++ project from scratch, focusing on designing and implementing a deep learning inference framework. The course supports latest models like LLama3.2 and Qwen2.5, and uses CUDA acceleration and Int8 quantization for better performance.

By taking this course, you will learn how to write efficient C++ code, manage projects with CMake and Git, design computational graphs, implement common operators like convolution and pooling, and optimize them for speed. This knowledge will be highly beneficial for job interviews and advancing your skills in deep learning. The course also includes practical demos on models like Unet and YoloV5, making it a hands-on learning experience.

https://github.com/zjhellofss/KuiperInfer

#go #cloud #cloud_management #graph #infrastructure_as_code #terraform

Terraform is a tool that helps you build, change, and manage your computer infrastructure safely and efficiently. It uses a simple code-like language to describe your infrastructure, so you can version and share it like any other code. Terraform shows you a plan of what it will do before making changes, avoids surprises, and works efficiently by managing resources in parallel. This reduces human errors and makes complex changes easier. You can learn more through tutorials and guides on the Terraform website, and even get certified to show off your skills. This helps you manage your infrastructure reliably and efficiently.

https://github.com/hashicorp/terraform

#python #deep_learning #geometric_deep_learning #graph_convolutional_networks #graph_neural_networks #pytorch

PyG (PyTorch Geometric) is a library that makes it easy to work with Graph Neural Networks (GNNs) using PyTorch. Here’s why it’s beneficial You can start training a GNN model with just 10-20 lines of code, especially if you're already familiar with PyTorch.
- **Comprehensive Models** The library supports large-scale graphs, dynamic graphs, and heterogeneous graphs, making it versatile for various applications.
- **Scalability** It provides extensive documentation, tutorials, and examples to help you get started quickly.

Overall, PyG simplifies the process of working with GNNs, making it a powerful tool for machine learning on graph-structured data.

https://github.com/pyg-team/pytorch_geometric