This repository contains an AI implementation that learns to play the classic game Snake in the terminal. The AI is built using Rust and Ratatui. Users can clone the repo, run the simulation, and configure various settings to customize the AI's behavior. The project also provides options for minimal UI, training custom networks, and watching the AI complete the game on different board sizes. The developer shares updates and insights about the project on Twitter and plans to create a detailed blog post explaining the AI's workings.

LLM SQL Generation Benchmark is a tool for evaluating different Large Language Models (LLMs) on their ability to generate accurate analytical SQL queries for Tinybird. It measures SQL query correctness, execution success, performance metrics, error handling, and recovery. The benchmark includes an automated retry mechanism for error correction. It supports various providers and models through OpenRouter and can be extended to other models. The benchmark is based on a GitHub dataset with 200M rows, where each LLM must produce SQL from 50 natural language prompts. Results are stored in JSON files and presented in a web application. Users can benchmark new models by following provided instructions.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

⌘ cmd is an open-source AI tool that seamlessly integrates into Xcode, allowing users to generate code from natural language, check its own work, and choose from a variety of AI models. It provides an agentic AI experience in Xcode, ensures data privacy by running locally, and supports multiple AI providers. Users can interact with cmd through Xcode or its own UI, and benefit from features like Claude Code integration and autonomous task delegation.

GuideLLM is a platform for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM enables users to assess the performance, resource requirements, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality. The tool provides features for performance evaluation, resource optimization, cost estimation, and scalability testing.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Bedrock Claude ChatBot is a Streamlit application that provides a conversational interface for users to interact with various Large Language Models (LLMs) on Amazon Bedrock. Users can ask questions, upload documents, and receive responses from the AI assistant. The app features conversational UI, document upload, caching, chat history storage, session management, model selection, cost tracking, logging, and advanced data analytics tool integration. It can be customized using a config file and is extensible for implementing specialized tools using Docker containers and AWS Lambda. The app requires access to Amazon Bedrock Anthropic Claude Model, S3 bucket, Amazon DynamoDB, Amazon Textract, and optionally Amazon Elastic Container Registry and Amazon Athena for advanced analytics features.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and fostering collaboration. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, configuration management, training job monitoring, media upload, and prediction. The repository also includes tutorial-style Jupyter notebooks demonstrating SDK usage.

AiDE is a lightweight framework for structuring AI-assisted development. It standardizes project context management, documentation, and collaboration, ensuring the assistant stays informed and productive throughout the project lifecycle. It offers drop-in simplicity with no dependencies, versatile usage for new and existing projects, and standardized templates for roadmaps, tasks, decisions, and sessions. The framework helps track project state, decision records, task management, and session tracking. It encourages best practices like starting each session by reviewing `.context` files, tracking task completion, documenting key decisions, and recording session summaries. The folder structure includes files for current state, roadmap, tasks, decisions, and sessions, with specific directories for active, completed, hold, and planned tasks. Contributions are welcome to enhance the usability of `.context`, and optional global rules for AI assistants are provided to optimize integration with the framework.

The Intel Gaudi Tutorials repository contains source files for tutorials on using PyTorch and PyTorch Lightning on the Intel Gaudi AI Processor. The tutorials cater to users from beginner to advanced levels and cover various tasks such as fine-tuning models, running inference, and setting up DeepSpeed for training large language models. Users need access to an Intel Gaudi 2 Accelerator card or node, run the Intel Gaudi PyTorch Docker image, clone the tutorial repository, install Jupyterlab, and run the Jupyterlab server to follow along with the tutorials.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

AgentIQ is a flexible library designed to seamlessly integrate enterprise agents with various data sources and tools. It enables true composability by treating agents, tools, and workflows as simple function calls. With features like framework agnosticism, reusability, rapid development, profiling, observability, evaluation system, user interface, and MCP compatibility, AgentIQ empowers developers to move quickly, experiment freely, and ensure reliability across agent-driven projects.

Unity Catalog is an open and interoperable catalog for data and AI, supporting multi-format tables, unstructured data, and AI assets. It offers plugin support for extensibility and interoperates with Delta Sharing protocol. The catalog is fully open with OpenAPI spec and OSS implementation, providing unified governance for data and AI with asset-level access control enforced through REST APIs.

Scale's LLM Engine is an open-source Python library, CLI, and Helm chart that provides everything you need to serve and fine-tune foundation models, whether you use Scale's hosted infrastructure or do it in your own cloud infrastructure using Kubernetes.

This repository provides a ready-to-run basic Slack AI solution that allows users to summarize threads and channels using OpenAI. Users can generate thread summaries, channel overviews, channel summaries since a specific time, and full channel summaries. The tool is powered by GPT-3.5-Turbo and an ensemble of NLP models. It requires Python 3.8 or higher, an OpenAI API key, Slack App with associated API tokens, Poetry package manager, and ngrok for local development. Users can customize channel and thread summaries, run tests with coverage using pytest, and contribute to the project for future enhancements.

Forza Mods AIO is a free and open-source tool that enhances the gaming experience in Forza Horizon 4 and 5. It offers a range of time-saving and quality-of-life features, making gameplay more enjoyable and efficient. The tool is designed to streamline various aspects of the game, improving user satisfaction and overall enjoyment.

The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.

Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.

MaterialSearch is a tool for searching local images and videos using natural language. It provides functionalities such as text search for images, image search for images, text search for videos (providing matching video clips), image search for videos (searching for the segment in a video through a screenshot), image-text similarity calculation, and Pexels video search. The tool can be deployed through the source code or Docker image, and it supports GPU acceleration. Users can configure the tool through environment variables or a .env file. The tool is still under development, and configurations may change frequently. Users can report issues or suggest improvements through issues or pull requests.

Tenere is a TUI interface for Language Model Libraries (LLMs) written in Rust. It provides syntax highlighting, chat history, saving chats to files, Vim keybindings, copying text from/to clipboard, and supports multiple backends. Users can configure Tenere using a TOML configuration file, set key bindings, and use different LLMs such as ChatGPT, llama.cpp, and ollama. Tenere offers default key bindings for global and prompt modes, with features like starting a new chat, saving chats, scrolling, showing chat history, and quitting the app. Users can interact with the prompt in different modes like Normal, Visual, and Insert, with various key bindings for navigation, editing, and text manipulation.

OpenKore is a custom client and intelligent automated assistant for Ragnarok Online. It is a free, open source, and cross-platform program (Linux, Windows, and MacOS are supported). To run OpenKore, you need to download and extract it or clone the repository using Git. Configure OpenKore according to the documentation and run openkore.pl to start. The tool provides a FAQ section for troubleshooting, guidelines for reporting issues, and information about botting status on official servers. OpenKore is developed by a global team, and contributions are welcome through pull requests. Various community resources are available for support and communication. Users are advised to comply with the GNU General Public License when using and distributing the software.

QA-Pilot is an interactive chat project that leverages online/local LLM for rapid understanding and navigation of GitHub code repository. It allows users to chat with GitHub public repositories using a git clone approach, store chat history, configure settings easily, manage multiple chat sessions, and quickly locate sessions with a search function. The tool integrates with `codegraph` to view Python files and supports various LLM models such as ollama, openai, mistralai, and localai. The project is continuously updated with new features and improvements, such as converting from `flask` to `fastapi`, adding `localai` API support, and upgrading dependencies like `langchain` and `Streamlit` to enhance performance.

The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.