Developing a Real-Time Automated Trading Platform with Python
Aprenda a desarrollar una plataforma de trading automatizado en tiempo real utilizando Python, explorando sistemas multiagente, procesamiento de datos y visualización con herramientas como ZeroMQ, NumPy y PyQtGraph.
How Numbers Station Combines LLM, RAG, and Domain Knowledge for AI-Driven Data Analysis
Leverage AI for comprehensive data analysis by combining structured and unstructured data, using multi-agent systems, and building AI-powered assistants for actionable insights.
Billions of Agents - A Sneak Preview of the Future
Explore the future landscape shaped by billions of AI agents, offering insights into potential societal and technological transformations.
Build an AI Social Media Content Generator with LangGraph and Llama 3.1 - Tutorial
Transform technical content into engaging social media posts using LangChain and Groq. Build a multi-agent AI system, generate and refine content, implement AI-driven critiques, and visualize workflows with LangGraph.
RxEnvironments - Reactive Multi-Agent Environments
Explore reactive programming for multi-agent environments in Julia. Learn to model complex agent-environment interactions using RxEnvironments.jl, overcoming limitations of traditional reinforcement learning frameworks.
How to Build a Multi-Agent AI System Using Large Language Models
Learn to create specialized AI agents using Large Language Models for task automation, from task creation to keynote speech generation.
Portfolio Management Using Multi-Agent Reinforcement Learning
Explore automated stock portfolio management using Deep Learning Reinforcement and innovative indicators like news sentiment scores, enhancing trading productivity and profitability.
Creating and Publishing Multiple AI Agents to Production with Praison AI
Simplify AI agent creation and management with Praison AI, a low-code solution merging Autogen and Crew AI functionalities. Create powerful agents using a single page of text and deploy easily to Google Cloud.
PDL: A Declarative Language for Smarter and Faster LLM Programming
Discover how PDL revolutionizes prompt programming with a declarative YAML-based approach, enabling modular, reusable patterns for enhanced LLM interactions and sophisticated application development.
Multi-Agent AI Systems: From Imitation Learning to Markov Game Theory
Explore advanced game theory concepts in multi-agent AI systems, covering investment strategies, cybersecurity applications, and the evolution from reinforcement learning to imitation learning frameworks.
Multi-agent AI Systems: From Simple Rules to Complex Problem Solving
Explore how multi-agent AI systems offer a practical alternative to large-scale models, enabling flexible, explainable, and efficient solutions for real-world problems through distributed intelligence.
Building Intelligent Systems with MoE, Multimodality, and Crowd of Agents
Explore advanced AI systems using MoE, multimodality, and agent crowds. Learn techniques for building composite intelligent systems beyond single algorithms or models.
Outracing Champion Gran Turismo Drivers With Deep Reinforcement Learning
Explore deep reinforcement learning techniques used to train AI agents that outperform world-class e-sports drivers in Gran Turismo racing simulations.
Aligning AI to Everyone via Reinforcement Learning
Explore reinforcement learning techniques for aligning AI with human values, fostering cooperation and trust in intelligent decision-making systems.
RLHF: How to Learn from Human Feedback with Reinforcement Learning
Explore reinforcement learning techniques for incorporating human feedback in AI systems, enhancing multi-agent and human-AI interactions in social contexts.
Concordia Contest 2024 - Basic Agent Development Tutorial
Develop cooperative AI agents for text-based environments using the Concordia framework. Advance language model intelligence in open-ended worlds similar to tabletop RPGs.
Self-Play in Artificial Intelligence - Lecture by Noam Brown
Explore advanced AI techniques in imperfect-information games, focusing on self-play algorithms and their applications in poker and beyond.
LLM Projects - A Quick Tutorial on Multi-Agent Workflows with AutoGen
Explore multi-agent workflows using AutoGen framework for RAG-based question answering with Wikipedia integration. Learn to implement and leverage this powerful approach for dynamic information retrieval.
AutoGen: Hype vs. Reality - Limitations for Real-World Applications
Explore AutoGen's capabilities and limitations in multi-agent workflows, distinguishing hype from reality. Gain insights into its potential and current constraints for real-world applications.
Why I'm Staying Away from Crew AI - A Critical Analysis
Explore limitations of Crew AI for production, understand multi-hop questions, and learn alternative approaches for building AI applications. Gain insights on future multi-agent frameworks.
Getting Started with LangGraph - Building Stateful Multi-AI Agents - Tutorial 1
Learn to create stateful, multi-actor LLM applications using LangGraph. Explore graph structure, state management, and coordination for sophisticated agent runtimes extending LangChain capabilities.