#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
#python
**What is nanoGPT?**
nanoGPT is a simple and fast way to train medium-sized GPT models. It's based on minGPT but focuses more on speed and ease of use. You can easily train new models or fine-tune existing ones like GPT-2. The code is simple and easy to understand, making it great for developers who want to quickly work with GPT models. It uses popular libraries like PyTorch and Hugging Face's transformers, making it easy to customize and use on different devices, including GPUs and CPUs.
**Benefits for Users** Simple code makes it easy to modify and train models.
- **Fast Training** Allows users to train new models or fine-tune existing ones.
https://github.com/karpathy/nanoGPT
#python
Using NVIDIA H100 GPUs for training large language models (LLMs) offers significant benefits. These GPUs are designed to speed up AI tasks, especially with their Transformer Engine and FP8 precision, which can train models like GPT-3 up to four times faster than previous generations[1][4]. The H100 GPUs are highly efficient and cost-effective, making them ideal for cloud-based environments[2][6]. They also support various AI frameworks, ensuring compatibility and ease of use for developers[7]. This setup allows for faster model convergence and reduced training time, making it a powerful tool for AI research and development.
https://github.com/KellerJordan/modded-nanogpt
#python
You can set up and run the Gemini 2.5 Computer Use model to automate browser tasks by cloning its repository, creating a Python virtual environment, installing dependencies, and configuring API keys for Gemini or Vertex AI. This model "sees" the browser screen via screenshots and performs actions like clicking or typing, mimicking human interaction. You run it using a command-line script where you give natural language instructions, and it executes them in a browser environment locally or via Browserbase. This helps automate repetitive tasks, testing, data collection, and more, saving time and reducing errors in web workflows.
https://github.com/google/computer-use-preview
#python
Zen MCP Server lets you use your favorite command-line AI tools like Claude Code, Gemini CLI, or Codex CLI together with many AI models (Gemini, OpenAI, Anthropic, and more) in one smooth workflow. It connects these models so they can collaborate, share context, and specialize in tasks like code review, debugging, or planning, making your coding smarter and faster. You stay in control, choosing which AI to use and when, while Zen handles context continuity and model orchestration. This means better code quality, deeper insights, and efficient teamwork from multiple AI experts all within your CLI environment.
https://github.com/BeehiveInnovations/zen-mcp-server