AI-Security-and-Privacy-Events is a curated list of academic events focusing on AI security and privacy. It includes seminars, conferences, workshops, tutorials, special sessions, and covers various topics such as NLP & LLM Security, Privacy and Security in ML, Machine Learning Security, AI System with Confidential Computing, Adversarial Machine Learning, and more.

Fueling-Ambitions-Via-Book-Discoveries is an Advanced Machine Learning & AI Course designed for students, professionals, and AI researchers. The course integrates rigorous theoretical foundations with practical coding exercises, ensuring learners develop a deep understanding of AI algorithms and their applications in finance, healthcare, robotics, NLP, cybersecurity, and more. Inspired by MIT, Stanford, and Harvard’s AI programs, it combines academic research rigor with industry-standard practices used by AI engineers at companies like Google, OpenAI, Facebook AI, DeepMind, and Tesla. Learners can learn 50+ AI techniques from top Machine Learning & Deep Learning books, code from scratch with real-world datasets, projects, and case studies, and focus on ML Engineering & AI Deployment using Django & Streamlit. The course also offers industry-relevant projects to build a strong AI portfolio.

CyberSentinel AI is a powerful automated security monitoring and AI analysis system designed to help security researchers and enthusiasts track the latest security vulnerabilities (CVE) and security-related repositories on GitHub in real-time. It utilizes artificial intelligence technology for in-depth analysis and automatically publishes valuable security intelligence to a blogging platform. The system features multiple data sources monitoring, intelligent AI analysis using OpenAI and Gemini engines, fully automated workflow with 24/7 monitoring, daily briefings, and dynamic blacklists, flexible configuration and management with support for multiple tokens, configurable parameters, and detailed logging, and automatic blog publishing with integrated blogging platform and Markdown reports.

This repository provides a comprehensive guide to understanding and applying artificial intelligence (AI) theory, including basic knowledge, machine learning, deep learning, and natural language processing (BERT). It features detailed explanations, annotated code, and datasets to help users grasp the concepts and implement them in practice. The repository is continuously updated to ensure the latest information and best practices are covered.

LLM-Navigation is a repository dedicated to documenting learning records related to large models, including basic knowledge, prompt engineering, building effective agents, model expansion capabilities, security measures against prompt injection, and applications in various fields such as AI agent control, browser automation, financial analysis, 3D modeling, and tool navigation using MCP servers. The repository aims to organize and collect information for personal learning and self-improvement through AI exploration.

Academic Forge is a collection of skills integrated for academic writing workflows. It provides a curated set of skills related to academic writing and research, allowing for precise skill calls, avoiding confusion between similar skills, maintaining focus on research workflows, and receiving timely updates from original authors. The forge integrates carefully selected skills covering various areas such as bioinformatics, clinical research, data analysis, scientific writing, laboratory automation, machine learning, databases, AI research, model architectures, fine-tuning, post-training, distributed training, optimization, inference, evaluation, agents, multimodal tasks, and machine learning paper writing. It is designed to streamline the academic writing and AI research processes by providing a cohesive and community-driven collection of skills.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

KCORES LLM Arena is a large model evaluation tool that focuses on real-world scenarios, using human scoring and benchmark testing to assess performance. It aims to provide an unbiased evaluation of large models in real-world applications. The tool includes programming ability tests and specific benchmarks like Mandelbrot Set, Mars Mission, Solar System, and Ball Bouncing Inside Spinning Heptagon. It supports various programming languages and emphasizes performance optimization, rendering, animations, physics simulations, and creative implementations.

Embodied-AI-Guide is a comprehensive guide for beginners to understand Embodied AI, focusing on the path of entry and useful information in the field. It covers topics such as Reinforcement Learning, Imitation Learning, Large Language Model for Robotics, 3D Vision, Control, Benchmarks, and provides resources for building cognitive understanding. The repository aims to help newcomers quickly establish knowledge in the field of Embodied AI.

This repository, named Awesome-Embodied-Agent-with-LLMs, is a curated list of research related to Embodied AI or agents with Large Language Models. It includes various papers, surveys, and projects focusing on topics such as self-evolving agents, advanced agent applications, LLMs with RL or world models, planning and manipulation, multi-agent learning and coordination, vision and language navigation, detection, 3D grounding, interactive embodied learning, rearrangement, benchmarks, simulators, and more. The repository provides a comprehensive collection of resources for individuals interested in exploring the intersection of embodied agents and large language models.

Wegent is an open-source AI-native operating system designed to define, organize, and run intelligent agent teams. It offers various core features such as a chat agent with multi-model support, conversation history, group chat, attachment parsing, follow-up mode, error correction mode, long-term memory, sandbox execution, and extensions. Additionally, Wegent includes a code agent for cloud-based code execution, AI feed for task triggers, AI knowledge for document management, and AI device for running tasks locally. The platform is highly extensible, allowing for custom agents, agent creation wizard, organization management, collaboration modes, skill support, MCP tools, execution engines, YAML config, and an API for easy integration with other systems.

XLN (Cross-Local Network) is a platform that enables instant off-chain settlement with on-chain finality. It combines Byzantine consensus, Bloomberg Terminal functionalities, and VR capabilities to run economic simulations in the browser without the need for a backend. The architecture includes layers for jurisdictions, entities, and accounts, with features like Solidity contracts, BFT consensus, and bilateral channels. The tool offers a panel system similar to Bloomberg Terminal for workspace organization and visualization, along with support for offline blockchain simulations in the browser and VR/Quest compatibility.

This repository provides a comprehensive overview of large language models (LLMs), covering various aspects such as their history, types, underlying architecture, training techniques, and applications. It includes detailed explanations of key concepts like Transformer models, distributed training, fine-tuning, and reinforcement learning. The repository also discusses the evaluation and limitations of LLMs, including the phenomenon of hallucinations. Additionally, it provides a list of related courses and references for further exploration.

This repository is a collection of free large language models (LLMs) that can be used for various natural language processing tasks. It includes information on different free LLM APIs and projects that can be deployed without cost. Users can find details on the performance, login requirements, function calling capabilities, and deployment environments of each listed LLM source.

ALwrity is a lightweight and user-friendly text analysis tool designed for developers and data scientists. It provides various functionalities for analyzing and processing text data, including sentiment analysis, keyword extraction, and text summarization. With ALwrity, users can easily gain insights from their text data and make informed decisions based on the analysis results. The tool is highly customizable and can be integrated into existing workflows seamlessly, making it a valuable asset for anyone working with text data in their projects.

Pipecat-examples is a collection of example applications built with Pipecat, an open-source framework for building voice and multimodal AI applications. It includes various examples demonstrating telephony & voice calls, web & client applications, realtime APIs, multimodal & creative solutions, translation & localization tasks, support, educational & specialized use cases, advanced features, deployment & infrastructure setups, monitoring & analytics tools, and testing & development scenarios.

AI-Security-and-Privacy-Events is a curated list of academic events focusing on AI security and privacy. It includes seminars, conferences, workshops, tutorials, special sessions, and covers various topics such as NLP & LLM Security, Privacy and Security in ML, Machine Learning Security, AI System with Confidential Computing, Adversarial Machine Learning, and more.

Open Computer Use is a secure cloud Linux computer powered by E2B Desktop Sandbox and controlled by open-source LLMs. It allows users to operate the computer via keyboard, mouse, and shell commands, live stream the display of the sandbox on the client computer, and pause or prompt the agent at any time. The tool is designed to work with any operating system and supports integration with various LLMs and providers following the OpenAI API specification.

CodeGate is a local gateway that enhances the safety of AI coding assistants by ensuring AI-generated recommendations adhere to best practices, safeguarding code integrity, and protecting individual privacy. Developed by Stacklok, CodeGate allows users to confidently leverage AI in their development workflow without compromising security or productivity. It works seamlessly with coding assistants, providing real-time security analysis of AI suggestions. CodeGate is designed with privacy at its core, keeping all data on the user's machine and offering complete control over data.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

SynapseML (previously known as MMLSpark) is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. It provides simple, composable, and distributed APIs for various machine learning tasks such as text analytics, vision, anomaly detection, and more. Built on Apache Spark, SynapseML allows seamless integration of models into existing workflows. It supports training and evaluation on single-node, multi-node, and resizable clusters, enabling scalability without resource wastage. Compatible with Python, R, Scala, Java, and .NET, SynapseML abstracts over different data sources for easy experimentation. Requires Scala 2.12, Spark 3.4+, and Python 3.8+.

MLX-VLM is a package designed for running Vision LLMs on Mac systems using MLX. It provides a convenient way to install and utilize the package for processing large language models related to vision tasks. The tool simplifies the process of running LLMs on Mac computers, offering a seamless experience for users interested in leveraging MLX for vision-related projects.

The Java-AI-Book-Code repository contains code examples for the 2020 edition of 'Practical Artificial Intelligence With Java'. It is a comprehensive update of the previous 2013 edition, featuring new content on deep learning, knowledge graphs, anomaly detection, linked data, genetic algorithms, search algorithms, and more. The repository serves as a valuable resource for Java developers interested in AI applications and provides practical implementations of various AI techniques and algorithms.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

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.