Winter is a UCI chess engine that has competed at top invite-only computer chess events. It is the top-rated chess engine from Switzerland and has a level of play that is super human but below the state of the art reached by large, distributed, and resource-intensive open-source projects like Stockfish and Leela Chess Zero. Winter has relied on many machine learning algorithms and techniques over the course of its development, including certain clustering methods not used in any other chess programs, such as Gaussian Mixture Models and Soft K-Means. As of Winter 0.6.2, the evaluation function relies on a small neural network for more precise evaluations.

Bagatur chess engine is a powerful Java chess engine that can run on Android devices and desktop computers. It supports the UCI protocol and can be easily integrated into chess programs with user interfaces. The engine is available for download on various platforms and has advanced features like SMP (multicore) support and NNUE evaluation function. Bagatur also includes syzygy endgame tablebases and offers various UCI options for customization. The project started as a personal challenge to create a chess program that could defeat a friend, leading to years of development and improvements.

DeepSeek is a new experimental architecture for a large language model (LLM) powered internet-scale retrieval engine. Unlike current research agents designed as answer engines, DeepSeek aims to process a vast amount of sources to collect a comprehensive list of entities and enrich them with additional relevant data. The end result is a table with retrieved entities and enriched columns, providing a comprehensive overview of the topic. DeepSeek utilizes both standard keyword search and neural search to find relevant content, and employs an LLM to extract specific entities and their associated contents. It also includes a smaller answer agent to enrich the retrieved data, ensuring thoroughness. DeepSeek has the potential to revolutionize research and information gathering by providing a comprehensive and structured way to access information from the vastness of the internet.

Commonplace Bot is a modern representation of the commonplace book, leveraging modern technological advancements in computation, data storage, machine learning, and networking. It aims to capture, engage, and share knowledge by providing a platform for users to collect ideas, quotes, and information, organize them efficiently, engage with the data through various strategies and triggers, and transform the data into new mediums for sharing. The tool utilizes embeddings and cached transformations for efficient data storage and retrieval, flips traditional engagement rules by engaging with the user, and enables users to alchemize raw data into new forms like art prompts. Commonplace Bot offers a unique approach to knowledge management and creative expression.

TFTMuZeroAgent is an implementation of a purely artificial intelligence algorithm to play Teamfight Tactics, an auto chess game made by Riot. It uses a simulation of TFT Set 4 and the MuZero reinforcement learning algorithm. The project provides a multi-agent petting zoo environment where players, pool, and game round classes are designed for AI project. The implementation excludes graphics and sounds but covers all aspects of the game from set 4. The codebase is open for contributions and improvements, allowing for additional models to be added to the environment.

seemore is a vision language model developed in Pytorch, implementing components like image encoder, vision-language projector, and decoder language model. The model is built from scratch, including attention mechanisms and patch creation. It is designed for readability and hackability, with the intention to be improved upon. The implementation is based on public publications and borrows attention mechanism from makemore by Andrej Kapathy. The code was developed on Databricks using a single A100 for compute, and MLFlow is used for tracking metrics. The tool aims to provide a simplistic version of vision language models like Grok 1.5/GPT-4 Vision, suitable for experimentation and learning.

Moatless Tools is a hobby project focused on experimenting with using Large Language Models (LLMs) to edit code in large existing codebases. The project aims to build tools that insert the right context into prompts and handle responses effectively. It utilizes an agentic loop functioning as a finite state machine to transition between states like Search, Identify, PlanToCode, ClarifyChange, and EditCode for code editing tasks.

qlora-pipe is a pipeline parallel training script designed for efficiently training large language models that cannot fit on one GPU. It supports QLoRA, LoRA, and full fine-tuning, with efficient model loading and the ability to load any dataset that Axolotl can handle. The script allows for raw text training, resuming training from a checkpoint, logging metrics to Tensorboard, specifying a separate evaluation dataset, training on multiple datasets simultaneously, and supports various models like Llama, Mistral, Mixtral, Qwen-1.5, and Cohere (Command R). It handles pipeline- and data-parallelism using Deepspeed, enabling users to set the number of GPUs, pipeline stages, and gradient accumulation steps for optimal utilization.

Thinker is an AI improvement mod for Alpha Centauri: Alien Crossfire that enhances single player challenge and gameplay with features like improved production/movement AI, visual changes on map rendering, more config options, resolution settings, and automation features. It includes Scient's patches and requires the GOG version of Alpha Centauri with the official Alien Crossfire patch version 2.0 installed. The mod provides additional DLL features developed in C++ for a richer gaming experience.

This repository is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai. The material is open source and free. The course covers causality in model-based machine learning, Bayesian modeling, interventions, counterfactual reasoning, and deep causal latent variable models. It aims to equip learners with the ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations.

This repository contains raw airport files intended as a starting point to create new airport files for the game Endless ATC. Users can contribute by customizing airport files and submitting pull requests. The repository also welcomes markdown files with gameplay and development tips. Contributors are encouraged to join the Discord server for assistance and information.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

ReVA (Reverse Engineering Assistant) is a project aimed at building a disassembler agnostic AI assistant for reverse engineering tasks. It utilizes a tool-driven approach, providing small tools to the user to empower them in completing complex tasks. The assistant is designed to accept various inputs, guide the user in correcting mistakes, and provide additional context to encourage exploration. Users can ask questions, perform tasks like decompilation, class diagram generation, variable renaming, and more. ReVA supports different language models for online and local inference, with easy configuration options. The workflow involves opening the RE tool and program, then starting a chat session to interact with the assistant. Installation includes setting up the Python component, running the chat tool, and configuring the Ghidra extension for seamless integration. ReVA aims to enhance the reverse engineering process by breaking down actions into small parts, including the user's thoughts in the output, and providing support for monitoring and adjusting prompts.

TypeChat is a library that simplifies the creation of natural language interfaces using types. Traditionally, building natural language interfaces has been challenging, often relying on complex decision trees to determine intent and gather necessary inputs for action. Large language models (LLMs) have simplified this process by allowing us to accept natural language input from users and match it to intent. However, this has introduced new challenges, such as the need to constrain the model's response for safety, structure responses from the model for further processing, and ensure the validity of the model's response. Prompt engineering aims to address these issues, but it comes with a steep learning curve and increased fragility as the prompt grows in size.

Linesight is a reinforcement learning project focused on advancing AI capabilities in the racing game Trackmania. It aims to push the boundaries of AI performance by utilizing deep learning algorithms to achieve human-level driving and beat world records on official campaign tracks. The project provides an interface to interact with Trackmania Nations Forever programmatically, enabling tasks such as sending inputs, retrieving car states, and capturing screenshots. With a strong emphasis on equality of input devices, Linesight serves as a benchmark for testing various reinforcement learning algorithms in a challenging and dynamic gaming environment.

The Local Assistant Examples repository is a collection of educational examples showcasing the use of large language models (LLMs). It was initially created for a blog post on building a RAG model locally, and has since expanded to include more examples and educational material. Each example is housed in its own folder with a dedicated README providing instructions on how to run it. The repository is designed to be simple and educational, not for production use.

Winter is a UCI chess engine that has competed at top invite-only computer chess events. It is the top-rated chess engine from Switzerland and has a level of play that is super human but below the state of the art reached by large, distributed, and resource-intensive open-source projects like Stockfish and Leela Chess Zero. Winter has relied on many machine learning algorithms and techniques over the course of its development, including certain clustering methods not used in any other chess programs, such as Gaussian Mixture Models and Soft K-Means. As of Winter 0.6.2, the evaluation function relies on a small neural network for more precise evaluations.

Black Marlin is a UCI compliant chess engine fully written in Rust by Doruk Sekercioglu. It supports Chess960 and features a variety of search algorithms, pruning techniques, and evaluation methods. Black Marlin is designed to be efficient and accurate, and it has been shown to perform well against other top chess engines.

Camera Chess Web is a tool that allows you to use your phone camera to replace chess eBoards. With Camera Chess Web, you can broadcast your game to Lichess, play a game on Lichess, or digitize a chess game from a video or live stream. Camera Chess Web is free to download on Google Play.

Winter is a UCI chess engine that has competed at top invite-only computer chess events. It is the top-rated chess engine from Switzerland and has a level of play that is super human but below the state of the art reached by large, distributed, and resource-intensive open-source projects like Stockfish and Leela Chess Zero. Winter has relied on many machine learning algorithms and techniques over the course of its development, including certain clustering methods not used in any other chess programs, such as Gaussian Mixture Models and Soft K-Means. As of Winter 0.6.2, the evaluation function relies on a small neural network for more precise evaluations.

Black Marlin is a UCI compliant chess engine fully written in Rust by Doruk Sekercioglu. It supports Chess960 and features a variety of search algorithms, pruning techniques, and evaluation methods. Black Marlin is designed to be efficient and accurate, and it has been shown to perform well against other top chess engines.