upskill.om
Intermediate 6 hrs · 13 lessons

Local AI Development

Run powerful language models on your own machine. No API costs, no cloud, no data leaving your hardware. Build private AI apps that work offline.

Local AI Ollama LM Studio LLM Privacy Open Source AI
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About this path

Take yourself from zero to a working local AI stack on your own laptop. Install Ollama and LM Studio, pull and run open models like Llama 3, Gemma, and Mistral, then connect them to Python code, an Open WebUI chat interface, and your own custom tools. By the end, you will own your AI: no API keys, no per-token fees, no data leaving your machine. Built for developers who care about privacy, cost, and control.

Path outline

Module 1

Foundations: Why Local AI

Understand the privacy, cost, and freedom advantages of running models on your own hardware.

3 lessons
  • 1 Cloud AI vs Local AI: When Each Wins 8 min
  • 2 Hardware Reality Check: What You Actually Need 10 min
  • 3 Meet the Tools: Ollama, LM Studio, llama.cpp 12 min

Module 2

Install Ollama & Run Your First Model

Get Ollama running on your machine and have a real conversation with a local model in under fifteen minutes.

3 lessons
  • 1 Install Ollama on macOS, Windows, or Linux 15 min
  • 2 Pull and Run Your First Model 12 min
  • 3 Choosing the Right Model for the Job 18 min

Module 3

Visual Interfaces: LM Studio & Open WebUI

Move beyond the terminal. Browse models visually, chat in a polished interface, and serve a private ChatGPT-style app.

2 lessons
  • 1 LM Studio: Visual Model Discovery 14 min
  • 2 Open WebUI: Your Private ChatGPT 25 min

Module 4

Programming With Local Models

Connect your code to Ollama. Build a chatbot, a JSON extractor, and a PDF question-answering tool — all running on your hardware.

3 lessons
  • 1 The Ollama REST API: Everything Speaks OpenAI 16 min
  • 2 Build a Python Chatbot in 30 Lines 20 min
  • 3 Structured Output: Force JSON From Any Model 18 min

Module 5

Capstone: Private Document Q&A

Tie everything together. Build a tool that ingests your PDFs and answers questions about them — completely offline, completely private.

2 lessons
  • 1 RAG Explained: Giving Your Model Memory 12 min
  • 2 Build It: PDF Question-Answering, 100% Local 30 min