Spider is a high-performance web crawler and indexer designed to handle data curation workloads efficiently. It offers features such as concurrency, streaming, decentralization, headless Chrome rendering, HTTP proxies, cron jobs, subscriptions, smart mode, blacklisting, whitelisting, budgeting depth, dynamic AI prompt scripting, CSS scraping, and more. Users can easily get started with the Spider Cloud hosted service or set up local installations with spider-cli. The tool supports integration with Node.js and Python for additional flexibility. With a focus on speed and scalability, Spider is ideal for extracting and organizing data from the web.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

Hollama is a minimal web-UI tool designed for interacting with Ollama servers. It features large prompt fields, streams completions, ability to copy completions as raw text, Markdown parsing with syntax highlighting, and saves sessions/context in the browser's localStorage. Users can access the latest version of Hollama at https://hollama.fernando.is without sign up, and data is stored locally on the browser. The tool can also be run as a Docker image by executing a specific command. Developers can connect to an Ollama server by updating the ORIGIN settings. Hollama facilitates easy development by providing instructions to set up the environment, install dependencies, and start a development server. Building a production version of the app is straightforward with a single command, and deployment may require installing an adapter for the target environment.

Dify is an open-source LLM app development platform that combines AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more. It allows users to quickly go from prototype to production. Key features include: 1. Workflow: Build and test powerful AI workflows on a visual canvas. 2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions. 3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features. 4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval. 5. Agent capabilities: Define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools. 6. LLMOps: Monitor and analyze application logs and performance over time. 7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs for easy integration into your own business logic.

Chat Chat is a unified chat and search to AI platform with a simple and easy-to-use interface. It supports major AI providers such as Anthropic, OpenAI, Cohere, and Google Gemini, and is easy to self-host. Chat Chat can be used for a variety of tasks, including searching for information, getting help with writing, and translating languages.

DevChat is an open-source workflow engine that enables developers to create intelligent, automated workflows for engaging with users through a chat panel within their IDEs. It combines script writing flexibility, latest AI models, and an intuitive chat GUI to enhance user experience and productivity. DevChat simplifies the integration of AI in software development, unlocking new possibilities for developers.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

E.D.D.I (Enhanced Dialog Driven Interface) is an enterprise-certified chatbot middleware that offers advanced prompt and conversation management for Conversational AI APIs. Developed in Java using Quarkus, it is lean, RESTful, scalable, and cloud-native. E.D.D.I is highly scalable and designed to efficiently manage conversations in AI-driven applications, with seamless API integration capabilities. Notable features include configurable NLP and Behavior rules, support for multiple chatbots running concurrently, and integration with MongoDB, OAuth 2.0, and HTML/CSS/JavaScript for UI. The project requires Java 21, Maven 3.8.4, and MongoDB >= 5.0 to run. It can be built as a Docker image and deployed using Docker or Kubernetes, with additional support for integration testing and monitoring through Prometheus and Kubernetes endpoints.

ComposeAI is an Android & iOS application similar to ChatGPT, built using Compose Multiplatform. It utilizes various technologies such as Compose Multiplatform, Material 3, OpenAI Kotlin, Voyager, Koin, SQLDelight, Multiplatform Settings, Coil3, Napier, BuildKonfig, Firebase Analytics & Crashlytics, and AdMob. The app architecture follows Google's latest guidelines. Users need to set up their own OpenAI API key before using the app.

Geoweaver is an in-browser software that enables users to easily compose and execute full-stack data processing workflows using online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides server management, code repository, workflow orchestration software, and history recording capabilities. Users can run it from both local and remote machines. Geoweaver aims to make data processing workflows manageable for non-coder scientists and preserve model run history. It offers features like progress storage, organization, SSH connection to external servers, and a web UI with Python support.

The go2coding.github.io repository is a collection of resources for AI enthusiasts, providing information on AI products, open-source projects, AI learning websites, and AI learning frameworks. It aims to help users stay updated on industry trends, learn from community projects, access learning resources, and understand and choose AI frameworks. The repository also includes instructions for local and external deployment of the project as a static website, with details on domain registration, hosting services, uploading static web pages, configuring domain resolution, and a visual guide to the AI tool navigation website. Additionally, it offers a platform for AI knowledge exchange through a QQ group and promotes AI tools through a WeChat public account.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

GPTMe is a tool that allows users to interact with an LLM assistant directly in their terminal in a chat-style interface. The tool provides features for the assistant to run shell commands, execute code, read/write files, and more, making it suitable for various development and terminal-based tasks. It serves as a local alternative to ChatGPT's 'Code Interpreter,' offering flexibility and privacy when using a local model. GPTMe supports code execution, file manipulation, context passing, self-correction, and works with various AI models like GPT-4. It also includes a GitHub Bot for requesting changes and operates entirely in GitHub Actions. In progress features include handling long contexts intelligently, a web UI and API for conversations, web and desktop vision, and a tree-based conversation structure.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Aria is a Zotero plugin that serves as an AI Research Assistant powered by Large Language Models (LLMs). It offers features like drag-and-drop referencing, autocompletion for creators and tags, visual analysis using GPT-4 Vision, and saving chats as notes and annotations. Aria requires the OpenAI GPT-4 model family and provides a configurable interface through preferences. Users can install Aria by downloading the latest release from GitHub and activating it in Zotero. The tool allows users to interact with Zotero library through conversational AI and probabilistic models, with the ability to troubleshoot errors and provide feedback for improvement.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

Time-LLM is a reprogramming framework that repurposes large language models (LLMs) for time series forecasting. It allows users to treat time series analysis as a 'language task' and effectively leverage pre-trained LLMs for forecasting. The framework involves reprogramming time series data into text representations and providing declarative prompts to guide the LLM reasoning process. Time-LLM supports various backbone models such as Llama-7B, GPT-2, and BERT, offering flexibility in model selection. The tool provides a general framework for repurposing language models for time series forecasting tasks.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

Transformers_And_LLM_Are_What_You_Dont_Need is a repository that explores the limitations of transformers in time series forecasting. It contains a collection of papers, articles, and theses discussing the effectiveness of transformers and LLMs in this domain. The repository aims to provide insights into why transformers may not be the best choice for time series forecasting tasks.

PyTorch Forecasting is a PyTorch-based package for time series forecasting with state-of-the-art network architectures. It offers a high-level API for training networks on pandas data frames and utilizes PyTorch Lightning for scalable training on GPUs and CPUs. The package aims to simplify time series forecasting with neural networks by providing a flexible API for professionals and default settings for beginners. It includes a timeseries dataset class, base model class, multiple neural network architectures, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. PyTorch Forecasting is built on pytorch-lightning for easy training on various hardware configurations.

Spider is a high-performance web crawler and indexer designed to handle data curation workloads efficiently. It offers features such as concurrency, streaming, decentralization, headless Chrome rendering, HTTP proxies, cron jobs, subscriptions, smart mode, blacklisting, whitelisting, budgeting depth, dynamic AI prompt scripting, CSS scraping, and more. Users can easily get started with the Spider Cloud hosted service or set up local installations with spider-cli. The tool supports integration with Node.js and Python for additional flexibility. With a focus on speed and scalability, Spider is ideal for extracting and organizing data from the web.

AI for Science paper collection is an initiative by AI for Science Community to collect and categorize papers in AI for Science areas by subjects, years, venues, and keywords. The repository contains `.csv` files with paper lists labeled by keys such as `Title`, `Conference`, `Type`, `Application`, `MLTech`, `OpenReviewLink`. It covers top conferences like ICML, NeurIPS, and ICLR. Volunteers can contribute by updating existing `.csv` files or adding new ones for uncovered conferences/years. The initiative aims to track the increasing trend of AI for Science papers and analyze trends in different applications.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.