A hybrid AI honeypot for monitoring large scale web attacks

A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.

Hybrid AI is the future of explainable intelligence. This article explores how combining vector search, knowledge graphs, and retrieval-augmented generation (RAG) creates AI systems that can reason, cite, and explain their answers with insights learned from building a real Graph-Powered RAG Engine.

A modular toolkit for designing, analyzing, and validating hybrid AI systems using Boxology visual patterns.

⚡ Production-ready .NET Standard 2.1 RAG library with 🤖 multi-AI provider support, 🏢 enterprise vector storage, 📄 intelligent document processing, and 🗄️ multi-database query coordination. 🌍 Cross-platform compatible.

GA + LLM hybrid framework for structured text generation and task optimization.

Multi-AI Agent Energy Management System with HILS simulation, Hybrid AI (ML+LLM), and MCP Runtime - Real-time visualization demo for smart grid optimization

Turiya is a Self-Evolving Neuro-Symbolic AI that runs entirely on CPU. It learns autonomously from the web, builds a hybrid vector + logic knowledge base, performs reasoning using symbolic and neural methods, and evolves its world model through sleep-like consolidation cycles. A fully local, event-driven cognitive architecture.

A Hybrid AI Agent for Quantitative Football Prediction Analysis

Neuroplastic database architecture for AI memory systems. 233x faster, 80% less memory

Гибридная модель ИИ для автошахмат, сочетающая формальные эвристики и адаптивное поведение.

A hybrid AI model for predicting failures in water distribution systems using Adaptive Neuro-Fuzzy Inference System (ANFIS). The model integrates Genetic Algorithms (GA) and Ant Colony Optimization (ACO) to improve the accuracy of accident prediction.

SymRAG adaptively routes queries through neuro-symbolic, neural, or hybrid paths based on complexity and system load, ensuring efficient and accurate RAG for diverse QA tasks.

Hybrid AI orchestration stack combining local LLMs (Ollama), vector search (Qdrant), and Azure AI Foundry for scalable RAG, Agentic AI, and Vision. Built with .NET 8 and Python.

Quantum Natural Language Processing (QNLP) using Quantum LSTM (QLSTM) architectures for advanced text classification tasks. This project demonstrates how quantum-inspired LSTM networks can be applied to natural language understanding and classification using Qiskit/PennyLane.

HAL, the Hybrid Artificial-Intelligence Layer, is the future of artificial intelligence. It's a layered approach that combines different AI techniques to create a more complex and adaptable system. HAL's layers work together to gather data from the environment, interpret it, learn from it, and make informed decisions based on that knowledge.

textual entailment experiments on Mednli dataset.

Hybrid Edge-Cloud Vision-Language System (VLM) for interactive semantic grounding and robotics perception. Powered by PaliGemma 2 and Gemini 3.0.

A Multimodal AI Memory Archive that preserves human personality using Hybrid RAG (Gemini + Llama 3) and Vector Embeddings. "To live in the hearts we leave behind is not to die."