AI Engineering

AI Engineering is a collection of practical AI projects, experiments, tutorials, and applied research notes focused on building and understanding real-world AI systems. This section covers applied machine learning, deep learning, foundation models, AI agents, and hands-on experimentation with modern AI tools.

The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes. These articles are written for AI learners, engineers, enterprise architects, researchers, and technology leaders who want to understand how AI systems are designed, tested, and applied in real-world scenarios.

Agentic AI for Beginners – Learn by Building Your First App

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🚀 Build Your First Agentic AI App (With Code Example!)

In this beginner-friendly bootcamp, you’ll learn how to build a multi-agent AI application from scratch. We’ll walk you through the core architecture of agentic systems and show you how to bring it to life with a real-world project — an AI-powered Workshop Planner and Meeting Assistant.

You’ll get:

✅ Step-by-step tutorial

💡 Ready-to-run Python code

🔧 Hands-on experience with LangChain and LangGraph

Just download the notebook, run it in your Python environment (like Google Colab), and start building. Tweak it, expand it, and you’re on your way to creating your own AI agents!

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How to Install and Run Your First Local LLM on Your Laptop

AI, AI Engineering /

Have you ever used ChatGPT or other GenAI tools online? Imagine running one of these smart language models directly on your laptop—no coding experience required. In this guide, I’ll show you how to install a local LLM (Large Language Model) like DeepSeek and LLama2, using Ollama and run it within a Jupyter Notebook. All you need is a laptop with an internet connection. Simply follow the steps and copy-paste the code. Let’s get started!

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