generative-ai

Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..

A Python framework for multi-modal document understanding with Amazon Bedrock

GenAI/RAG Optimizer and Toolkit for experimentation using Oracle Database AI Vector Search and NL2SQL

A visual node-based editor for building, sharing, and executing complex AI workflows with Fal.ai and Replicate.

Personalize the way Vietnamese learn English using generative AI

🤖 A curated list of websites that restrict access to AI Agents, AI crawlers and GPTs

Open-source implementation of Pomelli project by Google

A high performance C++ SDK for AI Agents

A sample Chatbot in C# using Microsoft Agent Framework

Prototype advanced LLM algorithms for reasoning and planning.

A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.

[ICML 2026] a unified reinforcement learning toolbox for joint RL on language models and diffusion models

Learn how to build generative AI applications on AWS using PartyRock, Amazon Bedrock, and Amazon Q

Use Stable Diffusion in Photopea!

82 ready-to-deploy agents for Microsoft Copilot Chat, no Copilot Premium required. Paste the instruction block into the Copilot Studio builder. Writing, HR, PM, IT ops, Sales, Finance, Engineering and more. RAI reviewed, source-quality labelled, tested. No coding.

[NeurIPS 2023] The repo of CommonScenes, a scene generation method powered by the diffusion model.

Build autonomous Python agents with native Agent-to-Agent (A2A) communication

AI视觉创作套件，集图解、绘画、编辑和视频生成于一体。支持Vercel一键部署

Training and inference code for ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design [ICLR 2025 oral]

This repo contains the codes of the penetration test benchmark for Generative Agents presented in the paper "AutoPenBench: Benchmarking Generative Agents for Penetration Testing". It contains also the instructions to install, develop and test new vulnerable containers to include in the benchmark.