Behavior3Lua is a Lua framework for behavior trees in game AI. It provides a modified blackboard system where behavior trees are designed like code editors, allowing game designers to configure logic through editing trees. The framework offers various node types for creating complex AI behaviors, freeing game programmers from manual configuration. It includes composite, decorator, and action nodes, along with an API for creating and running behavior trees. The framework supports running states and provides an editor for visual tree editing. It has been successfully used in multiple projects for different game genres, enabling designers to create sophisticated AI and logic systems.

Aio-scrapy is an asyncio-based web crawling and web scraping framework inspired by Scrapy. It supports distributed crawling/scraping, implements compatibility with scrapyd, and provides options for using redis queue and rabbitmq queue. The framework is designed for fast extraction of structured data from websites. Aio-scrapy requires Python 3.9+ and is compatible with Linux, Windows, macOS, and BSD systems.

Polyfire is an all-in-one managed backend for AI apps that allows users to build AI apps directly from the frontend, eliminating the need for a separate backend. It simplifies the process by providing most backend services in just a few lines of code. With Polyfire, users can easily create chatbots, transcribe audio files to text, generate simple text, create a long-term memory, and generate images with Dall-E. The tool also offers starter guides and tutorials to help users get started quickly and efficiently.

Java-Interview-Tutorial is a repository containing resources and tutorials for Java interview preparation. It provides guidance on setting up the project locally, adjusting image paths, and submitting articles. The repository also includes instructions for configuring the project and using Git GUI tools for managing content. Users can learn about Java concurrency programming and navigate through the content easily. The repository emphasizes clean article titles and content formatting to ensure proper display on the website.

The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.

Acte is a framework designed to build GUI-like tools for AI Agents. It aims to address the issues of cognitive load and freedom degrees when interacting with multiple APIs in complex scenarios. By providing a graphical user interface (GUI) for Agents, Acte helps reduce cognitive load and constraints interaction, similar to how humans interact with computers through GUIs. The tool offers APIs for starting new sessions, executing actions, and displaying screens, accessible via HTTP requests or the SessionManager class.

Solana Agent Kit is an open-source toolkit designed for connecting AI agents to Solana protocols. It enables agents, regardless of the model used, to autonomously perform various Solana actions such as trading tokens, launching new tokens, lending assets, sending compressed airdrops, executing blinks, and more. The toolkit integrates core blockchain features like token operations, NFT management via Metaplex, DeFi integration, Solana blinks, AI integration features with LangChain, autonomous modes, and AI tools. It provides ready-to-use tools for blockchain operations, supports autonomous agent actions, and offers features like memory management, real-time feedback, and error handling. Solana Agent Kit facilitates tasks such as deploying tokens, creating NFT collections, swapping tokens, lending tokens, staking SOL, and sending SPL token airdrops via ZK compression. It also includes functionalities for fetching price data from Pyth and relies on key Solana and Metaplex libraries for its operations.

The gpt-bitcoin repository is focused on creating an automated trading system for Bitcoin using GPT AI technology. It provides different versions of trading strategies utilizing various data sources such as OHLCV, Moving Averages, RSI, Stochastic Oscillator, MACD, Bollinger Bands, Orderbook Data, news data, fear/greed index, and chart images. Users can set up the system by creating a .env file with necessary API keys and installing required dependencies. The repository also includes instructions for setting up the environment on local machines and AWS EC2 Ubuntu servers. The future plan includes expanding the system to support other cryptocurrency exchanges like Bithumb, Binance, Coinbase, OKX, and Bybit.

Retro AIM Server is an instant messaging server that revives AOL Instant Messenger clients from the 2000s. It supports Windows AIM client versions 5.0-5.9, away messages, buddy icons, buddy list, chat rooms, instant messaging, user profiles, blocking/visibility toggle/idle notification, and warning. The Management API provides functionality for administering the server, including listing users, creating users, changing passwords, and listing active sessions.

MCP-Framework is a TypeScript framework for building Model Context Protocol (MCP) servers with automatic directory-based discovery for tools, resources, and prompts. It provides powerful abstractions, simple server setup, and a CLI for rapid development and project scaffolding.

aiotdlib is a Python asyncio Telegram client based on TDLib. It provides automatic generation of types and functions from tl schema, validation, good IDE type hinting, and high-level API methods for simpler work with tdlib. The package includes prebuilt TDLib binaries for macOS (arm64) and Debian Bullseye (amd64). Users can use their own binary by passing `library_path` argument to `Client` class constructor. Compatibility with other versions of the library is not guaranteed. The tool requires Python 3.9+ and users need to get their `api_id` and `api_hash` from Telegram docs for installation and usage.

ai-gradio is a Python package that simplifies the creation of machine learning apps using various models like OpenAI, Google's Gemini, Anthropic's Claude, LumaAI, CrewAI, XAI's Grok, and Hyperbolic. It provides easy installation with support for different providers and offers features like text chat, voice chat, video chat, code generation interfaces, and AI agent teams. Users can set API keys for different providers and customize interfaces for specific tasks.

A simple Golang package to access the Airtable API. It provides functionalities to interact with Airtable such as initializing client, getting tables, listing records, adding records, updating records, deleting records, and bulk deleting records. The package is compatible with Go 1.13 and above.

Hezar is an all-in-one AI library designed specifically for the Persian community. It brings together various AI models and tools, making it easy to use AI with just a few lines of code. The library seamlessly integrates with Hugging Face Hub, offering a developer-friendly interface and task-based model interface. In addition to models, Hezar provides tools like word embeddings, tokenizers, feature extractors, and more. It also includes supplementary ML tools for deployment, benchmarking, and optimization.

PocketGroq is a tool that provides advanced functionalities for text generation, web scraping, web search, and AI response evaluation. It includes features like an Autonomous Agent for answering questions, web crawling and scraping capabilities, enhanced web search functionality, and flexible integration with Ollama server. Users can customize the agent's behavior, evaluate responses using AI, and utilize various methods for text generation, conversation management, and Chain of Thought reasoning. The tool offers comprehensive methods for different tasks, such as initializing RAG, error handling, and tool management. PocketGroq is designed to enhance development processes and enable the creation of AI-powered applications with ease.

Behavior3Lua is a Lua framework for behavior trees in game AI. It provides a modified blackboard system where behavior trees are designed like code editors, allowing game designers to configure logic through editing trees. The framework offers various node types for creating complex AI behaviors, freeing game programmers from manual configuration. It includes composite, decorator, and action nodes, along with an API for creating and running behavior trees. The framework supports running states and provides an editor for visual tree editing. It has been successfully used in multiple projects for different game genres, enabling designers to create sophisticated AI and logic systems.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.