# Ai Engineering Hub

> In-depth tutorials on LLMs, RAG, AI Agents, MCP, and real-world AI engineering projects. 93+ production-ready projects.

- **Type:** MCP server
- **Install:** `agentstack add mcp-damn8daniel-ai-engineering-hub`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [damn8daniel](https://agentstack.voostack.com/s/damn8daniel)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [damn8daniel](https://github.com/damn8daniel)
- **Source:** https://github.com/damn8daniel/ai-engineering-hub

## Install

```sh
agentstack add mcp-damn8daniel-ai-engineering-hub
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AI Engineering Hub

### Your comprehensive resource for learning and building with AI

[](https://github.com/damn8daniel/ai-engineering-hub)
[](https://opensource.org/licenses/MIT)
[](http://makeapullrequest.com)

  
  
  
  
  
  

---

## Why This Repo?

AI Engineering is advancing rapidly, and staying at the forefront requires both deep understanding and hands-on experience. Here, you will find:

- **93+ Production-Ready Projects** across all skill levels
- **In-depth tutorials** on LLMs, RAG, Agents, and more
- **Real-world AI agent applications**
- **Examples to implement, adapt, and scale** in your projects

Whether you're a beginner, practitioner, or researcher, this repo provides resources for all skill levels to experiment and succeed in AI engineering.

---

## Table of Contents

- [Getting Started](#-getting-started)
- [Projects by Difficulty](#projects-by-difficulty)
  - [Beginner Projects (22)](#-beginner-projects)
  - [Intermediate Projects (48)](#-intermediate-projects)
  - [Advanced Projects (23)](#-advanced-projects)
- [AI Engineering Roadmap](#-ai-engineering-roadmap)
- [Contributing](#contributing)
- [License](#license)

---

## Getting Started

New to AI Engineering? Start here:

1. **Complete Beginners:** Check out the [AI Engineering Roadmap](./ai-engineering-roadmap) for a comprehensive learning path
2. **Learn the Basics:** Start with [Beginner Projects](#-beginner-projects) like OCR apps and simple RAG implementations
3. **Build Your Skills:** Move to [Intermediate Projects](#-intermediate-projects) with agents and complex workflows
4. **Master Advanced Concepts:** Tackle [Advanced Projects](#-advanced-projects) including fine-tuning and production systems

---

## Projects by Difficulty

### Beginner Projects

> Simple, self-contained projects to get started with AI engineering.

#### OCR & Vision

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [LaTeX OCR with Llama](./LaTeX-OCR-with-Llama) | Convert LaTeX equation images to code | Llama 3.2 Vision, Streamlit |
| [Llama OCR](./llama-ocr) | 100% local OCR application | Llama 3.2, Streamlit, Ollama |
| [Gemma-3 OCR](./gemma3-ocr) | Local OCR with structured text extraction | Gemma-3, Ollama |
| [Qwen 2.5 OCR](./qwen-2.5VL-ocr) | Text extraction from images | Qwen 2.5 VL |

#### Chat Interfaces & UI

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Local ChatGPT with DeepSeek](./local-chatgpt-deepseek) | Mini-ChatGPT with visible reasoning | DeepSeek-R1, Chainlit |
| [Local ChatGPT with Llama](./local-chatgpt-llama) | ChatGPT clone with vision | Llama 3.2, Streamlit |
| [Local ChatGPT with Gemma 3](./local-chatgpt-gemma3) | Local chat interface | Gemma 3, Ollama |
| [DeepSeek Thinking UI](./deepseek-thinking-ui) | ChatGPT with visible chain-of-thought | DeepSeek-R1, React |
| [Qwen3 Thinking UI](./qwen3-thinking-ui) | Thinking UI with streaming | Qwen3:4B, Streamlit |
| [GPT-OSS Thinking UI](./gpt-oss-thinking-ui) | Open-source GPT with reasoning viz | GPT-OSS, React |
| [Streaming AI Chatbot](./streaming-ai-chatbot) | Real-time AI streaming chatbot | Motia Framework |

#### Basic RAG

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Simple RAG Workflow](./simple-rag-workflow) | Basic RAG pipeline | LlamaIndex, Ollama |
| [Document Chat RAG](./document-chat-rag) | Chat with your documents | Llama 3.3, LangChain |
| [Fastest RAG Stack](./fastest-rag-stack) | Optimized RAG pipeline | SambaNova, LlamaIndex, Qdrant |
| [GitHub RAG](./github-rag) | Chat with GitHub repositories | LlamaIndex, Ollama |
| [ModernBERT RAG](./modernbert-rag) | RAG with modern embeddings | ModernBERT, FAISS |
| [Llama 4 RAG](./llama-4-rag) | RAG powered by Llama 4 | Llama 4, LangChain |

#### Multimodal & Media

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Image Generation with Janus-Pro](./imagegen-janus-pro) | Local image generation | DeepSeek Janus-Pro 7B |
| [Video RAG with Gemini](./video-rag-gemini) | Chat with videos | Gemini AI, LangChain |

#### Other Tools

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Website to API with FireCrawl](./website-to-api-firecrawl) | Convert websites to structured APIs | FireCrawl, FastAPI |
| [AI News Generator](./ai-news-generator) | Automated news generation | CrewAI, Cohere |
| [Siamese Network](./siamese-network) | Digit similarity detection | PyTorch, MNIST |

---

### Intermediate Projects

> Multi-component systems, agentic workflows, and advanced features for experienced practitioners.

#### AI Agents & Workflows

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [YouTube Trend Analysis](./youtube-trend-analysis) | Analyze YouTube trends | CrewAI, BrightData |
| [AutoGen Stock Analyst](./autogen-stock-analyst) | Advanced stock analyst agent | Microsoft AutoGen |
| [Agentic RAG](./agentic-rag) | RAG with document search and web fallback | LlamaIndex, Tavily |
| [Agentic RAG with DeepSeek](./agentic-rag-deepseek) | Enterprise agentic RAG | GroundX, DeepSeek |
| [Book Writer Flow](./book-writer-flow) | Automated book writing | CrewAI Flow |
| [Content Planner Flow](./content-planner-flow) | Content workflow automation | CrewAI Flow |
| [Brand Monitoring](./brand-monitoring) | Automated brand monitoring system | CrewAI, LangChain |
| [Hotel Booking Crew](./hotel-booking-crew) | Multi-agent hotel booking | DeepSeek-R1, CrewAI |
| [Deploy Agentic RAG](./deploy-agentic-rag) | Private Agentic RAG API | LitServe, LlamaIndex |
| [Zep Memory Assistant](./zep-memory-assistant) | AI Agent with human-like memory | Zep, LangChain |
| [Agent with MCP Memory](./agent-with-mcp-memory) | Agent with persistent graph memory | Graphiti, Opik |
| [ACP Code](./acp-code) | Agent Communication Protocol demo | ACP SDK |
| [Motia Content Creation](./motia-content-creation) | Social media content automation | Motia Framework |

#### Voice & Audio

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Real-time Voice Bot](./real-time-voicebot) | Travel guide voice bot | AssemblyAI, ElevenLabs |
| [RAG Voice Agent](./rag-voice-agent) | Real-time RAG voice agent | Cartesia, LlamaIndex |
| [Chat with Audios](./chat-with-audios) | RAG over audio files | Whisper, LangChain |
| [Audio Analysis Toolkit](./audio-analysis-toolkit) | Comprehensive audio analysis | AssemblyAI |
| [Multilingual Meeting Notes](./multilingual-meeting-notes) | Auto meeting notes with language detection | Whisper, GPT-4 |

#### Advanced RAG

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [RAG with Dockling](./rag-with-dockling) | RAG over Excel with IBM's Dockling | IBM Dockling, LangChain |
| [Trustworthy RAG](./trustworthy-rag) | RAG over complex docs with TLM | Cleanlab TLM |
| [Fastest RAG with Milvus and Groq](./fastest-rag-milvus-groq) | Sub-15ms retrieval latency | Milvus, Groq |
| [Chat with Code](./chat-with-code) | Chat with codebase | Qwen3-Coder |
| [RAG SQL Router](./rag-sql-router) | Agent with RAG and SQL routing | LangChain, PostgreSQL |

#### Multimodal

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [DeepSeek Multimodal RAG](./deepseek-multimodal-rag) | MultiModal RAG | DeepSeek-Janus-Pro |
| [ColiVara Website RAG](./colivara-website-rag) | MultiModal RAG for websites | ColiVara, DeepSeek |
| [Multimodal RAG with AssemblyAI](./multimodal-rag-assemblyai) | Audio + vector database + CrewAI | AssemblyAI, ChromaDB |

#### MCP - Model Context Protocol

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Cursor Linkup MCP](./cursor-linkup-mcp) | Custom MCP with deep web search | Linkup, Cursor |
| [EyeLevel MCP RAG](./eyelevel-mcp-rag) | MCP for RAG over complex docs | EyeLevel, Claude |
| [LlamaIndex MCP](./llamaindex-mcp) | Local MCP client | LlamaIndex, Ollama |
| [MCP Agentic RAG](./mcp-agentic-rag) | Agentic RAG via MCP | Claude, MCP SDK |
| [MCP Agentic RAG Firecrawl](./mcp-agentic-rag-firecrawl) | Web-aware agentic RAG | FireCrawl, MCP |
| [MCP Video RAG](./mcp-video-rag) | Video understanding via MCP | Gemini, MCP SDK |
| [MCP Voice Agent](./mcp-voice-agent) | Voice-controlled MCP agent | AssemblyAI, MCP |
| [SDV MCP](./sdv-mcp) | Synthetic Data Vault via MCP | SDV, MCP SDK |
| [KitOps MCP](./kitops-mcp) | ML model management via MCP | KitOps |
| [Stagehand x MCP-Use](./stagehand-mcp-use) | Web automation with MCP | Stagehand, Playwright |

#### Model Comparison & Evaluation

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [Evaluation and Observability](./eval-and-observability) | LLM evaluation pipeline | CometML Opik |
| [Llama 4 vs DeepSeek-R1](./llama4-vs-deepseek-r1) | Model benchmark comparison | Ollama, Python |
| [Qwen3 vs DeepSeek-R1](./qwen3-vs-deepseek-r1) | Reasoning model comparison | Ollama |
| [O3 vs Claude Code](./o3-vs-claude-code) | Coding model comparison | OpenAI, Anthropic |
| [Sonnet4 vs O4](./sonnet4-vs-o4) | Claude vs GPT comparison | Anthropic, OpenAI |
| [Sonnet4 vs Qwen3-Coder](./sonnet4-vs-qwen3-coder) | Coding benchmark | Anthropic, Qwen |
| [Code Model Comparison](./code-model-comparison) | Multi-model code benchmark | Various |
| [GPT-OSS vs Qwen3](./gpt-oss-vs-qwen3) | Open-source model battle | GPT-OSS, Qwen3 |

---

### Advanced Projects

> Production-grade systems, fine-tuning, and cutting-edge AI implementations.

#### Fine-tuning & Model Development

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [DeepSeek Fine-tuning](./deepseek-finetuning) | Fine-tune DeepSeek models | Unsloth, Ollama |
| [Build Reasoning Model](./build-reasoning-model) | Build DeepSeek-R1-like reasoning | GRPO, Unsloth |
| [Attention Is All You Need](./attention-impl) | Transformer from scratch | PyTorch |

#### Advanced Agent Systems

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [NVIDIA NIM Demo](./nvidia-nim-demo) | CrewAI Flows + NVIDIA NIM | CrewAI, NVIDIA NIM |
| [Documentation Writer Flow](./documentation-writer-flow) | Automated doc generation | CrewAI Flow |
| [Multi-Agent Deep Researcher](./multi-agent-deep-researcher) | Multi-agent research system | MCP, CrewAI |
| [Multiplatform Deep Researcher](./multiplatform-deep-researcher) | Cross-platform research | BrightData, LangChain |
| [Web Browsing Agent](./web-browsing-agent) | AI web browsing automation | CrewAI, Stagehand |
| [Paralegal Agent Crew](./paralegal-agent-crew) | Legal RAG paralegal agent | CrewAI, LlamaIndex |
| [FireCrawl Agent](./firecrawl-agent) | Corrective RAG agent | FireCrawl, LangGraph |
| [Context Engineering Workflow](./context-engineering-workflow) | Production context pipeline | TensorLake, Zep |
| [Parlant Conversational Agent](./parlant-conversational-agent) | Compliance-driven chatbot | Parlant Framework |
| [Stock Portfolio Analysis Agent](./stock-portfolio-agent) | Full-stack portfolio analyzer | React, CrewAI |
| [Guidelines vs Traditional Prompt](./guidelines-vs-traditional-prompt) | Prompt engineering comparison | Various LLMs |

#### Advanced MCP & Infrastructure

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [MindsDB MCP](./mindsdb-mcp) | Unified MCP for all data sources | MindsDB |
| [Financial Analyst DeepSeek](./financial-analyst-deepseek) | Financial analysis agent | DeepSeek, MCP |
| [Graphiti MCP](./graphiti-mcp) | Persistent memory with knowledge graphs | Zep Graphiti |
| [Pixeltable MCP](./pixeltable-mcp) | Multimodal data orchestration | Pixeltable |
| [Ultimate AI Assistant](./ultimate-ai-assistant) | Multi-MCP server orchestration | Claude, Multiple MCPs |

#### Production Systems

| Project | Description | Tech Stack |
|---------|-------------|------------|
| [GroundX Document Pipeline](./groundx-doc-pipeline) | Production document processing | GroundX API |
| [NotebookLM Clone](./notebooklm-clone) | RAG with citations and podcasts | LangChain, TTS |

---

## AI Engineering Roadmap

New to AI Engineering? Follow the [complete roadmap](./ai-engineering-roadmap) with 10 stages:

| Stage | Topic | Resource |
|-------|-------|----------|
| 1 | Master Python | Harvard CS50p |
| 2 | AI with Python | DeepLearning.AI |
| 3 | Maths for ML | Khan Academy |
| 4 | Understanding LLMs | 3Blue1Brown |
| 5 | LLM Research | Andrej Karpathy |
| 6 | AI Agents | Anthropic Guide |
| 7 | Applied AI | CrewAI on Coursera |
| 8 | AI Protocols (MCP) | MCP Guidebook |
| 9 | Project-based Learning | This Repo |
| 10 | Books | AI Engineering - Chip Huyen |

```
Foundation     ━━▶  Master Python  ━━▶  AI with Python
                                              │
Mathematics    ━━▶  Maths for ML  ◀━━━━━━━━━━┘
                         │
Understanding  ━━▶  Understanding LLMs  ━━▶  LLM Research
                                                    │
Application    ━━▶  AI Agents  ━━▶  Applied AI  ━━▶  MCP
                                                      │
Mastery        ━━▶  Project-based Learning  ━━▶  Books
```

---

## Tech Stack Overview

| Category | Technologies |
|----------|-------------|
| **LLMs** | Llama 4, DeepSeek-R1, Gemma 3, Qwen 3, GPT-4, Claude |
| **RAG** | LlamaIndex, LangChain, Qdrant, Milvus, ChromaDB, FAISS |
| **Agents** | CrewAI, AutoGen, LangGraph, Swarm |
| **MCP** | MCP SDK (Python/TypeScript), Claude, Cursor |
| **Voice** | AssemblyAI, Cartesia, ElevenLabs, Whisper |
| **Fine-tuning** | Unsloth, PEFT, LoRA, QLoRA |
| **Frontend** | Streamlit, Chainlit, React, Gradio |
| **Infrastructure** | Ollama, LitServe, Docker, FastAPI |

---

## Contributing

Contributions are welcome! Here's how:

1. Fork this repository
2. Create a feature branch (`git checkout -b feature/amazing-project`)
3. Add your project in the appropriate difficulty folder
4. Include a README.md with setup instructions
5. Submit a Pull Request

### Project README Template

Each project should include:
- Description and use case
- Architecture diagram (if applicable)
- Prerequisites and setup instructions
- Step-by-step usage guide
- Example outputs/screenshots

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

**If you find this helpful, please give it a star!**

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [damn8daniel](https://github.com/damn8daniel)
- **Source:** [damn8daniel/ai-engineering-hub](https://github.com/damn8daniel/ai-engineering-hub)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-damn8daniel-ai-engineering-hub
- Seller: https://agentstack.voostack.com/s/damn8daniel
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.