generative-ai

A curated list of awesome Google Gemini Omni prompt guides, interactive platforms, and creative showcases.

Taught by AI genius Andrew NG, this course entails the cutting edge topics such as, How generative AI works including what it can and can't do, Common uses cases such as Reading, Writing, and Chatting, Life Cycle of GenAI projects, Advanced Technology options such as RAG, Fine tunning, and Pre-Training, Implications of GenAI on business & Society.

This repository contains hybrid-rag a LLMOPS python package

Transforming Gemini Notebook (formerly NotebookLM) into a versatile bot

Multi-model AI agent runtime. Define agents in YAML, route each role to a model, orchestrate with 7 patterns (ReAct, Plan & Execute, Fan-Out, Pipeline, Supervisor, Swarm, Glyph), and deploy as a REST/WebSocket API with RAG, memory, MCP tools, guardrails and OpenTelemetry observability.

Using embeddings to create memory.

The World Weaver System: True AI character consistency using Textual Inheritance. Maintain unshakeable character identity (face, body, essence) across radical changes in pose, clothing, and scene without LoRAs, IP-Adapters, or ControlNet. This repo contains the Character Vault and Prompt Helper components.

Swift 6 agent SDK: type-safe tools, streaming, cloud + on-device inference via MLX on Apple Silicon

Light-MER for efficient multimodal emotion recognition with sub-1B MLLMs.

An open-source, AI-driven Social Media Digital Twin powered by LLMs (Ollama, vLLM) for Computational Social Science simulations, network analysis, and human-agent interaction.

A complete, hands-on roadmap to becoming an AI Engineer, from Python basics to production RAG, Agents, and Fine-Tuning. 10 modules, 20+ notebooks, 15 real projects. Free, open source, built for the community.

All-in-one UI for merged LLMs in Hugging Face

Building an Image-to-Video (I2V) Model from Scratch

Automate the creation of soccer match highlights with the power of Generative AI and AWS. This solution leverages AWS Bedrock (Anthropic’s Claude 3 Sonnet model), AWS MediaConvert, Lambda, Step Functions and other AWS services to identify and compile exciting game moments without manual editing.

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.

A curated list of the best AI resources, courses, tools, and repositories. Covering Generative AI (GenAI), LLMs, RAG, AI Agents, Prompt Engineering, and more. Your go-to cheat sheet for all things AI.

Study Guide for the AI-102: Designing and Implementing a Microsoft Azure AI Solution Exam

Flan-t5-base model was fine-tuned on Nvidia Question and Answer Pair Dataset available on Kaggle. This is a beginner level project who wants to step in to the world of Large Language Models.

Using langchain framework created some useful Ai applications.

Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS.