Wendy is an operating system and developer platform for Physical AI — built to make it dramatically easier to build and deploy on NVIDIA Jetson, Raspberry Pi, and other edge devices.
— Maximilian Alexander (@signalgaining) 14 avril 2026
Today I’m incredibly excited to announce Wendy. Wendy is an operating system and developer platform for Physical AI — built to make it dramatically easier to build and deploy on NVIDIA Jetson, Raspberry Pi, and other edge devices. We think robotics, edge AI, industrial systems, autonomous machines, and smart cameras should be far simpler to create. Less setup. Less infrastructure pain. Faster time to first demo. This is the start of something big. Get started at wendy.sh
1974 Machine Learning! @bigdataconf A Greater Foundation for Machine Learning. Welcome to Python, TensorFlow, and PyTorch. Register and join me in the new year. Join and learn at your own pace. Machine Learning for all the ages. @bigdataconf #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Greater-MLearn
GPT with LangGraph! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode The rapid advancement of large model technology is leading to an increasing application of agent technology across various fields and industries significantly transforming how people work and live. In complex and dynamic environments, multi-agent systems are able to tackle intricate tasks that would be challenging for a single agent, thanks to their collaborative and division-of-labor approaches. The following stack of research papers and hands on tutorials highlight the integrated use of GPT with LangGraph and CrewAI. LangGraph enhances information transmission efficiency through its graph-based structure, while CrewAI boosts team collaboration and system performance via intelligent task allocation and resource management. The key areas of this research include: The design of agent architectures based on LangGraph for precise control . The enhancement of agent capabilities through CrewAI to tackle a range of tasks. The goal of this study is to explore the combined potential of GPT and LangGraph and CrewAI in multi-agent systems, offering fresh insights for the ongoing evolution of agent technology and fostering innovation in the application of large model intelligent agents. References Duan, Z., & Wang, J. (2024, November 27). Exploration of LLM multi-agent application implementation based on LangGraph+CrewAI. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2411.18241 Horsey, J. (2025, March 9). Build a powerful Python chatbot in minutes with LangGraph. Geeky Gadgets. Retrieved March 9, 2025, from geeky-gadgets.com/build-a-po… Ong, R. (2024, July 10). GPT-4o and LangGraph tutorial: Build a TNT-LLM application. DataCamp. Retrieved March 9, 2025, from datacamp.com/tutorial/gpt-4o… Sivan, V. (2024). Building AI agent systems with LangGraph. Medium. Retrieved March 9, 2025, from medium.com/pythoneers/buildi… Wang, J., & Duan, Z. (2024, December 2). Intelligent Spark agents: A modular LangGraph framework for scalable, visualized, and enhanced big data machine learning workflows. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2412.01490
