Eridanus is a powerful data visualization tool designed to help users create interactive and insightful visualizations from their datasets. With a user-friendly interface and a wide range of customization options, Eridanus makes it easy for users to explore and analyze their data in a meaningful way. Whether you are a data scientist, business analyst, or student, Eridanus provides the tools you need to communicate your findings effectively and make data-driven decisions.

AIDE-Plus is a comprehensive tool for Android app development, offering support for various Java syntax versions, Gradle and Maven build systems, ProGuard, AndroidX, CMake builds, APK/AAB generation, code coloring customization, data binding, and APK signing. It also provides features like AAPT2, D8, runtimeOnly, compileOnly, libgdxNatives, manifest merging, Shizuku installation support, and syntax auto-completion. The tool aims to streamline the development process and enhance the user experience by addressing common issues and providing advanced functionalities.

ProChat is a components library focused on quickly building large language model chat interfaces. It empowers developers to create rich, dynamic, and intuitive chat interfaces with features like automatic chat caching, streamlined conversations, message editing tools, auto-rendered Markdown, and programmatic controls. The tool also includes design evolution plans such as customized dialogue rendering, enhanced request parameters, personalized error handling, expanded documentation, and atomic component design.

Witsy is a generative AI desktop application that supports various models like OpenAI, Ollama, Anthropic, MistralAI, Google, Groq, and Cerebras. It offers features such as chat completion, image generation, scratchpad for content creation, prompt anywhere functionality, AI commands for productivity, expert prompts for specialization, LLM plugins for additional functionalities, read aloud capabilities, chat with local files, transcription/dictation, Anthropic Computer Use support, local history of conversations, code formatting, image copy/download, and more. Users can interact with the application to generate content, boost productivity, and perform various AI-related tasks.

LLMFarm is an iOS and MacOS app designed to work with large language models (LLM). It allows users to load different LLMs with specific parameters, test the performance of various LLMs on iOS and macOS, and identify the most suitable model for their projects. The tool is based on ggml and llama.cpp by Georgi Gerganov and incorporates sources from rwkv.cpp by saharNooby, Mia by byroneverson, and LlamaChat by alexrozanski. LLMFarm features support for MacOS (13+) and iOS (16+), various inferences and sampling methods, Metal compatibility (not supported on Intel Mac), model setting templates, LoRA adapters support, LoRA finetune support, LoRA export as model support, and more. It also offers a range of inferences including LLaMA, GPTNeoX, Replit, GPT2, Starcoder, RWKV, Falcon, MPT, Bloom, and others. Additionally, it supports multimodal models like LLaVA, Obsidian, and MobileVLM. Users can customize inference options through JSON files and access supported models for download.

BMW MD is a simple WhatsApp user BOT created by Ibrahim Tech. It allows users to scan pairing codes or QR codes to connect to WhatsApp and deploy the bot on Heroku. The bot can be used to perform various tasks such as sending messages, receiving messages, and managing contacts. It is released under the MIT License and contributions are welcome.

RealScaler is a Windows app powered by RealESRGAN AI to enhance, upscale, and de-noise photos and videos. It provides an easy-to-use GUI for upscaling images and videos using multiple AI models. The tool supports automatic image tiling and merging to avoid GPU VRAM limitations, resizing images/videos before upscaling, interpolation between original and upscaled content, and compatibility with various image and video formats. RealScaler is written in Python and requires Windows 11/10, at least 8GB RAM, and a Directx12 compatible GPU with 4GB VRAM. Future versions aim to enhance performance, support more GPUs, offer a new GUI with Windows 11 style, include audio for upscaled videos, and provide features like metadata extraction and application from original to upscaled files.

QualityScaler is a Windows app powered by AI to enhance, upscale, and de-noise photographs and videos. It provides an easy-to-use GUI for upscaling images and videos using multiple AI models. The tool supports automatic image tiling and merging to avoid GPU VRAM limitations, resizing images/videos before upscaling, and interpolation between the original and upscaled content. QualityScaler is written in Python and utilizes external packages such as torch, onnxruntime-directml, customtkinter, OpenCV, moviepy, and nuitka. It requires Windows 11 or Windows 10, at least 8GB of RAM, and a Directx12 compatible GPU with 4GB VRAM or more. The tool aims to continue improving with upcoming versions by adding new features, enhancing performance, and supporting additional AI architectures.

FluidFrames.RIFE is a Windows app powered by RIFE AI to create frame-generated and slowmotion videos. It is written in Python and utilizes external packages such as torch, onnxruntime-directml, customtkinter, OpenCV, moviepy, and Nuitka. The app features an elegant GUI, video frame generation at different speeds, video slow motion, video resizing, multiple GPU support, and compatibility with various video formats. Future versions aim to support different GPU types, enhance the GUI, include audio processing, optimize video processing speed, and introduce new features like saving AI-generated frames and supporting different RIFE AI models.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

Feast is an open source feature store for machine learning, providing a fast path to manage infrastructure for productionizing analytic data. It allows ML platform teams to make features consistently available, avoid data leakage, and decouple ML from data infrastructure. Feast abstracts feature storage from retrieval, ensuring portability across different model training and serving scenarios.

LearnPrompt is a permanent, free, open-source AIGC course platform that currently supports various tools like ChatGPT, Agent, Midjourney, Runway, Stable Diffusion, AI digital humans, AI voice & music, and large model fine-tuning. The platform offers features such as multilingual support, comment sections, daily selections, and submissions. Users can explore different modules, including sound cloning, RAG, GPT-SoVits, and OpenAI Sora world model. The platform aims to continuously update and provide tutorials, examples, and knowledge systems related to AI technologies.

Airi is a VTuber project heavily inspired by Neuro-sama. It is capable of various functions such as playing Minecraft, chatting in Telegram and Discord, audio input from browser and Discord, client side speech recognition, VRM and Live2D model support with animations, and more. The project also includes sub-projects like unspeech, hfup, Drizzle ORM driver for DuckDB WASM, and various other tools. Airi uses models like whisper-large-v3-turbo from Hugging Face and is similar to projects like z-waif, amica, eliza, AI-Waifu-Vtuber, and AIVTuber. The project acknowledges contributions from various sources and implements packages to interact with LLMs and models.

A curated list of awesome Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list focuses on production-ready and experimental servers extending AI capabilities through file access, database connections, API integrations, and other contextual services.

AIMr is an AI aimbot tool written in Python that leverages modern technologies to achieve an undetected system with a pleasing appearance. It works on any game that uses human-shaped models. To optimize its performance, users should build OpenCV with CUDA. For Valorant, additional perks in the Discord and an Arduino Leonardo R3 are required.

Univer is an isomorphic full-stack framework designed for creating and editing spreadsheets, documents, and slides across web and server. It is highly extensible, high-performance, and can be embedded into applications. Univer offers a wide range of features including formulas, conditional formatting, data validation, collaborative editing, printing, import & export, and more. It supports multiple languages and provides a distraction-free editing experience with a clean interface. Univer is suitable for data analysts, software developers, project managers, content creators, and educators.

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.

PyTorch Forecasting is a PyTorch-based package designed for state-of-the-art timeseries forecasting using deep learning architectures. It offers a high-level API and leverages PyTorch Lightning for efficient training on GPU or CPU with automatic logging. The package aims to simplify timeseries forecasting tasks by providing a flexible API for professionals and user-friendly defaults for beginners. It includes features such as a timeseries dataset class for handling data transformations, missing values, and subsampling, various neural network architectures optimized for real-world deployment, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. Built on pytorch-lightning, it supports training on CPUs, single GPUs, and multiple GPUs out-of-the-box.

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.

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

Vanna is an open-source Python framework for SQL generation and related functionality. It uses Retrieval-Augmented Generation (RAG) to train a model on your data, which can then be used to ask questions and get back SQL queries. Vanna is designed to be portable across different LLMs and vector databases, and it supports any SQL database. It is also secure and private, as your database contents are never sent to the LLM or the vector database.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

Avalonia-Assistant is an open-source desktop intelligent assistant that aims to provide a user-friendly interactive experience based on the Avalonia UI framework and the integration of Semantic Kernel with OpenAI or other large LLM models. By utilizing Avalonia-Assistant, you can perform various desktop operations through text or voice commands, enhancing your productivity and daily office experience.

Marvin is a lightweight AI toolkit for building natural language interfaces that are reliable, scalable, and easy to trust. Each of Marvin's tools is simple and self-documenting, using AI to solve common but complex challenges like entity extraction, classification, and generating synthetic data. Each tool is independent and incrementally adoptable, so you can use them on their own or in combination with any other library. Marvin is also multi-modal, supporting both image and audio generation as well using images as inputs for extraction and classification. Marvin is for developers who care more about _using_ AI than _building_ AI, and we are focused on creating an exceptional developer experience. Marvin users should feel empowered to bring tightly-scoped "AI magic" into any traditional software project with just a few extra lines of code. Marvin aims to merge the best practices for building dependable, observable software with the best practices for building with generative AI into a single, easy-to-use library. It's a serious tool, but we hope you have fun with it. Marvin is open-source, free to use, and made with 💙 by the team at Prefect.

Activepieces is an open source replacement for Zapier, designed to be extensible through a type-safe pieces framework written in Typescript. It features a user-friendly Workflow Builder with support for Branches, Loops, and Drag and Drop. Activepieces integrates with Google Sheets, OpenAI, Discord, and RSS, along with 80+ other integrations. The list of supported integrations continues to grow rapidly, thanks to valuable contributions from the community. Activepieces is an open ecosystem; all piece source code is available in the repository, and they are versioned and published directly to npmjs.com upon contributions. If you cannot find a specific piece on the pieces roadmap, please submit a request by visiting the following link: Request Piece Alternatively, if you are a developer, you can quickly build your own piece using our TypeScript framework. For guidance, please refer to the following guide: Contributor's Guide