DL-Hub is a deep learning repository containing various study materials and code projects in the fields of machine learning, deep learning, computer vision, natural language processing, and web crawling. It includes paper analysis, deep learning projects, graph neural network replications, machine learning algorithms, transformer models, and optimization implementations. The repository aims to provide valuable resources for learning and research in the deep learning and machine learning domains.

This repository is a comprehensive resource for AI Systems Performance Engineering, providing code examples, tools, and resources for GPU optimization, distributed training, inference scaling, and performance tuning. It covers a wide range of topics such as performance tuning mindset, system architecture, GPU programming, memory optimization, and the latest profiling tools. The focus areas include GPU architecture, PyTorch, CUDA programming, distributed training, memory optimization, and multi-node scaling strategies.

FiniteMonkey is an advanced vulnerability mining engine powered purely by GPT, requiring no prior knowledge base or fine-tuning. Its effectiveness significantly surpasses most current related research approaches. The tool is task-driven, prompt-driven, and focuses on prompt design, leveraging 'deception' and hallucination as key mechanics. It has helped identify vulnerabilities worth over $60,000 in bounties. The tool requires PostgreSQL database, OpenAI API access, and Python environment for setup. It supports various languages like Solidity, Rust, Python, Move, Cairo, Tact, Func, Java, and Fake Solidity for scanning. FiniteMonkey is best suited for logic vulnerability mining in real projects, not recommended for academic vulnerability testing. GPT-4-turbo is recommended for optimal results with an average scan time of 2-3 hours for medium projects. The tool provides detailed scanning results guide and implementation tips for users.

KWeaver is an open-source cognitive intelligence development framework that provides data scientists, application developers, and domain experts with the ability for rapid development, comprehensive openness, and high-performance knowledge network generation and cognitive intelligence large model framework. It offers features such as automated and visual knowledge graph construction, visualization and analysis of knowledge graph data, knowledge graph integration, knowledge graph resource management, large model prompt engineering and debugging, and visual configuration for large model access.

LlamaFarm is a comprehensive AI framework that empowers users to build powerful AI applications locally, with full control over costs and deployment options. It provides modular components for RAG systems, vector databases, model management, prompt engineering, and fine-tuning. Users can create differentiated AI products without needing extensive ML expertise, using simple CLI commands and YAML configs. The framework supports local-first development, production-ready components, strategy-based configuration, and deployment anywhere from laptops to the cloud.

AIGNE DocSmith is a powerful AI-driven documentation generation tool that automates the creation of detailed, structured, and multi-language documentation directly from source code. It intelligently analyzes codebase to generate a comprehensive document structure, populates content with high-quality AI-powered generation, supports seamless translation into 12+ languages, integrates with AIGNE Hub for large language models, offers Discuss Kit publishing, automatically updates documentation with source code changes, and allows for individual document optimization.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications. It offers core features such as AI process management, memory system, and I/O system. The platform includes built-in tools like Calculator, Web Search, Financial Analysis, File Management, and Research Tools. It also provides specialized assistant teams for Python and research tasks. LYRAIOS is built on a technical architecture comprising FastAPI backend, Streamlit frontend, Vector Database, PostgreSQL storage, and Docker support. It offers features like knowledge management, process control, and security & access control. The roadmap includes enhancements in core platform, AI process management, memory system, tools & integrations, security & access control, open protocol architecture, multi-agent collaboration, and cross-platform support.

Claude Code Agents is an open-source AI agent system designed to enhance development workflows by providing specialized AI agents for orchestration, resilience engineering, and organizational memory. These agents offer specialized expertise across technologies, AI system with organizational memory, and an agent orchestration system. The system includes features such as engineering excellence by design, advanced orchestration system, Task Master integration, live MCP integrations, professional-grade workflows, and organizational intelligence. It is suitable for solo developers, small teams, enterprise teams, and open-source projects. The system requires a one-time bootstrap setup for each project to analyze the tech stack, select optimal agents, create configuration files, set up Task Master integration, and validate system readiness.

AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework designed to provide deep insights into AI agents, Large Language Model (LLM) calls, and tool interactions. It offers robust logging, visualization, and evaluation capabilities to help debug and optimize AI applications with ease. With features like tracing LLM calls, monitoring agents and tools, tracking interactions, detailed metrics collection, flexible data storage, simple instrumentation, interactive dashboard, project management, execution graph visualization, and evaluation tools, AgentNeo empowers users to build efficient, cost-effective, and high-quality AI-driven solutions.

This project aims to integrate mainstream open-source large models to achieve the coordination and cooperation between different types of large models, providing comprehensive and flexible AI content generation services.

PromptX is a leading AI agent context platform that revolutionizes interaction design, enabling AI agents to become industry experts. It offers core capabilities such as an AI role creation platform, intelligent tool development platform, and cognitive memory system. PromptX allows users to easily discover experts, summon them for assistance, and engage in professional dialogues through natural conversations. The platform's core philosophy emphasizes treating AI as a person, enabling users to communicate naturally without the need for complex commands. With Nuwa Creation Workshop, users can design custom AI roles using meta-prompt technology, transforming abstract needs into concrete executable AI expert roles in just minutes.

Korea-Startups is a repository containing a comprehensive list of major tech companies and startups in Korea. It covers a wide range of industries such as mobility, local community trading, food tech, interior design, fintech, AI, natural language processing, computer vision, robotics, legal tech, and more. The repository provides detailed information about each company's field, key services, and unique features, showcasing the diverse and innovative startup ecosystem in Korea.

Awesome Mind Network is a curated collection of open-source resources, SDKs, and tools by Mind Network, empowering developers and researchers with privacy-preserving technologies, Agentic AI, and decentralized infrastructure.

Generative AI for Beginners - Java Edition is a comprehensive workshop that introduces users to the core concepts of generative AI, Java AI ecosystem, model context protocol, and practical applications. The course covers topics such as prompt engineering, embeddings & vector operations, retrieval-augmented generation, and responsible AI development. Users can explore real-world scenarios like chatbots, content generation, and ethical AI development. The workshop provides a hands-on learning experience with multi-language support and development environment setup guidance.

Aegra is a self-hosted AI agent backend platform that provides LangGraph power without vendor lock-in. Built with FastAPI + PostgreSQL, it offers complete control over agent orchestration for teams looking to escape vendor lock-in, meet data sovereignty requirements, enable custom deployments, and optimize costs. Aegra is Agent Protocol compliant and perfect for teams seeking a free, self-hosted alternative to LangGraph Platform with zero lock-in, full control, and compatibility with existing LangGraph Client SDK.

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

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.

Curated Transformers is a transformer library for PyTorch that provides state-of-the-art models composed of reusable components. It supports various transformer architectures, including encoders like ALBERT, BERT, and RoBERTa, and decoders like Falcon, Llama, and MPT. The library emphasizes consistent type annotations, minimal dependencies, and ease of use for education and research. It has been production-tested by Explosion and will be the default transformer implementation in spaCy 3.7.

Txtai is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation, and more. Txtai can stand alone or serve as a knowledge source for large language models (LLMs). Key features include vector search with SQL, object storage, topic modeling, graph analysis, multimodal indexing, embedding creation for various data types, pipelines powered by language models, workflows to connect pipelines, and support for Python, JavaScript, Java, Rust, and Go. Txtai is open-source under the Apache 2.0 license.

**bert4torch** is a high-level framework for training and deploying transformer models in PyTorch. It provides a simple and efficient API for building, training, and evaluating transformer models, and supports a wide range of pre-trained models, including BERT, RoBERTa, ALBERT, XLNet, and GPT-2. bert4torch also includes a number of useful features, such as data loading, tokenization, and model evaluation. It is a powerful and versatile tool for natural language processing tasks.

This is a production-grade knowledge Q&A chatbot implementation based on AWS services and the LangChain framework, with optimizations at various stages. It supports flexible configuration and plugging of vector models and large language models. The front and back ends are separated, making it easy to integrate with IM tools (such as Feishu).

OpenAI for Workers AI is a simple, quick, and dirty implementation of OpenAI's API on Cloudflare's new Workers AI platform. It allows developers to use the OpenAI SDKs with the new LLMs without having to rewrite all of their code. The API currently supports completions, chat completions, audio transcription, embeddings, audio translation, and image generation. It is not production ready but will be semi-regularly updated with new features as they roll out to Workers AI.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

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.