#python
Foundry is a toolkit that helps design new proteins using powerful AI models. It includes tools to generate protein structures (RFD3), predict how they fold (RF3), and design amino acid sequences that will form those structures (ProteinMPNN/LigandMPNN). All models work together using a common framework for handling molecular structures, making it easier to go from idea to designed protein. The benefit is that it gives a complete, flexible system to create custom proteins for research, medicine, or biotechnology, with clear instructions and examples to get started quickly.
https://github.com/RosettaCommons/foundry
#python
Claude Quickstarts offers ready-made projects that help you quickly build AI applications using the Claude API. You can create tools like a customer support agent, financial data analyst, computer control demo, or an autonomous coding agent by following simple setup steps and using your Claude API key. These projects come with clear instructions and can be customized to fit your needs, saving you time and effort in development. This helps you start building powerful AI apps faster and learn how to use Claude’s advanced features effectively. You also get access to helpful resources, community support, and opportunities to contribute improvements.
https://github.com/anthropics/claude-quickstarts
#python
VibeVoice is an open-source AI tool that creates natural-sounding, expressive audio with up to four different voices, perfect for making podcasts, audiobooks, or long conversations. It keeps each speaker’s voice consistent and handles smooth turn-taking, making the audio sound realistic and engaging. The tool can generate speech in English and Chinese, and even adds spontaneous emotion or singing. It’s free to use and helps creators produce high-quality audio quickly, but should be used responsibly to avoid misuse.
https://github.com/microsoft/VibeVoice
#python
The Social-Engineer Toolkit (SET) is an open-source penetration testing framework created by TrustedSec that helps security professionals test organizational defenses through social engineering attacks. SET provides pre-built attack vectors for phishing, credential harvesting, and website cloning, allowing testers to simulate realistic threats quickly and effectively. The toolkit runs on Linux and Mac OS X, with easy installation via pip or package managers. By using SET with proper authorization, security teams can identify human vulnerabilities in their defenses, understand how employees respond to social engineering tactics, and implement stronger security awareness training to protect against real-world attacks.
https://github.com/trustedsec/social-engineer-toolkit
#python
You can use an AI-powered call center solution built with Azure and OpenAI GPT to automate phone calls for tasks like insurance claims, IT support, and customer service. This system handles calls in multiple languages, streams conversations in real-time, resumes after disconnections, and stores data securely. It uses advanced AI models to understand complex information, manage sensitive data safely, and customize conversations to your needs. The solution scales easily on Azure, offers call recording, human fallback, and brand-specific voices, improving customer experience and reducing costs by automating routine calls while keeping quality and compliance high. This helps you provide 24/7 support efficiently and with personalized service.
https://github.com/microsoft/call-center-ai
#python
This project teaches you how to build a real-world AI research assistant that automatically finds, reads, and answers questions about academic papers using a technique called Retrieval-Augmented Generation (RAG)[1][2][3]. RAG works by first searching for the most relevant information from a large collection of documents, then using a language model to generate clear, accurate answers based on that information—this means you get answers that are up-to-date and grounded in real sources, not just what the AI remembers from its training[1][2][3]. The course is hands-on: each week, you add a new piece, starting with setting up the technical infrastructure, then building automated data pipelines to fetch and process papers, adding powerful search tools (first with keywords, then with AI-powered semantic search), and finally connecting everything to a local AI model that can chat with you and explain complex topics in simple language. By the end, you’ll have a working system you can use to quickly find and understand research papers, and you’ll gain the skills to build similar AI tools for any field—all while learning the best practices used by professional engineers. The main benefit is that you get practical, production-ready AI skills and a tool that makes research faster and more reliable, with answers you can trust because they come directly from the latest papers.
https://github.com/jamwithai/arxiv-paper-curator
#python
You can use Tinker and Tinker Cookbook to easily fine-tune large language models (LLMs) for your specific needs without managing complex training infrastructure. Tinker handles distributed training and uses efficient LoRA adapters to reduce costs and speed up customization. The Cookbook offers ready-made examples and tools for tasks like chat, math reasoning, and reinforcement learning, helping you quickly build and improve AI models. This means you can create AI that better fits your domain, runs faster, and follows your rules, all while saving time and computing resources. It’s great for researchers, developers, and teams wanting powerful, flexible AI customization.
https://github.com/thinking-machines-lab/tinker-cookbook
#python
This project offers free, open-source AI agents designed to help with trading research and automation. It includes tools that can analyze strategies, backtest ideas, monitor markets, and manage risk using advanced AI models. The main benefit is that it lets you test and improve trading strategies safely with historical data before using real money, helping you make smarter decisions and avoid common mistakes. Always remember that trading carries risk and no tool can guarantee profits.
https://github.com/moondevonyt/moon-dev-ai-agents
#python
This AWS DevOps course helps you learn AWS in 30 days. Each day focuses on a different topic, like setting up AWS accounts, using IAM for security, and working with EC2 instances. You'll also learn about networking, security, and how to deploy applications. The course includes projects, such as setting up a secure VPC and deploying a web application. By the end, you'll have practical skills and knowledge to work with AWS in a DevOps environment, which can help you get a job in this field.
https://github.com/iam-veeramalla/aws-devops-zero-to-hero
#python
NVIDIA Isaac Sim is a powerful simulation platform that helps you develop, test, and train AI-powered robots in realistic virtual environments using high-quality physics and graphics. It supports importing robot models, simulates accurate robot movements and sensors like cameras and LiDAR, and integrates with tools like ROS for robotics control. You can generate synthetic data, run reinforcement learning, and create digital twins to test robots before real-world deployment. This saves time and cost by allowing you to experiment and improve robot designs safely and efficiently in a virtual space, speeding up development and increasing success in real applications.
https://github.com/isaac-sim/IsaacSim
#python
torchforge is a PyTorch-based library designed to simplify reinforcement learning (RL) by separating algorithm design from infrastructure management. It offers clear RL building blocks that let you focus on creating and modifying RL algorithms without worrying about complex system details like resource handling or communication. It supports rapid research, easy customization, and scalable training across many GPUs. Although still experimental, it helps you run RL experiments more efficiently and flexibly, especially if you want to scale up or control training processes finely. This saves you time and effort, letting you concentrate on improving your RL models. Installation requires PyTorch 2.9.0 and related tools, with tutorials coming soon.
https://github.com/meta-pytorch/torchforge
#python
Kronos is an open-source AI model specially made to understand and predict financial market data called K-lines (candlestick charts) from over 45 global exchanges. It uses a unique method to turn complex market data into simpler tokens, then learns patterns with a powerful Transformer model. This helps Kronos forecast prices, volatility, and generate realistic synthetic data better than previous models. You can easily use Kronos to make forecasts with just a few lines of code, and it supports batch predictions for multiple assets. It also allows fine-tuning on your own data to improve accuracy for your specific market needs, making it a valuable tool for financial analysis and trading strategies.
https://github.com/shiyu-coder/Kronos