The AI Merge documents how AI fits into real software systems.
The focus is on end-to-end AI systems - from models and agents to infrastructure, deployment, monitoring, and long-term operation. This GitHub organization complements the newsletter and courses with concrete implementations, reference code, and system-level examples.
The emphasis is on building, engineering tradeoffs, and production realities.
- End-to-end AI system walkthroughs
- Practical agents, vision pipelines, and multimodal systems
- Infrastructure, inference, and deployment examples
- MLOps / LLMOps patterns for operating systems in production
All repositories aim to show how components connect inside a larger system.
Note
The work here is informed by real-world experience across AI research, systems engineering, and ML operations.
| Project | Description | Status |
|---|---|---|
| End-to-End Vision Agent System | Full system covering data pipelines, annotation, training, optimization, deployment, monitoring, and operations | Planned |
| Multimodal RAG System | Real-world system using LLMs, vision, retrieval, and LLMOps | Planned |
These projects are designed as complete systems, not standalone examples.