7 modules. 30 lessons. Understand open-source AI models — what they are, how they work, when to use them, and how to run them yourself. No engineering background needed. Just practical literacy for professionals who want to make informed decisions.
Most open-source AI content is either too technical or too shallow. This course gives you the knowledge to evaluate models, understand trade-offs, and make real decisions — without becoming a machine learning engineer.
Privacy, Independence, CostThe real reasons open models matter — and when they don't. An honest assessment, not a sales pitch.
How Models Actually WorkDense vs sparse, quantization, context windows, thinking models — explained through analogies, not equations.
Read Benchmarks Without Getting FooledHow benchmarks lie, what they actually measure, and how to evaluate models based on your real work.
Run Models on Your Own MachineWhat hardware you need, what software to use, and how to try models for $5 before investing in anything.
A Decision Framework That LastsModels change monthly. The skill to evaluate them doesn't. Learn the framework, not the leaderboard.
02 // Curriculum
7 Modules. Zero Jargon Required.
Each module builds your understanding step by step. You start with why open models matter and finish with a personal strategy for choosing and running them. Every module ends with a hands-on exercise.
01
The Open Source AI Landscape
Why open-source models matter. Who makes them and why. Why the core technology is more stable than headlines suggest.
Why Open-Source Models Matter
Who Makes Open Models and Why
The Car Engine That Hasn't Changed
Exercise: Map Your AI Usage
02
How Models Are Built
Architecture decisions explained through analogies. Dense vs sparse, quantization as MP3 compression, context windows, and thinking models.
Dense vs Sparse: The Big Architecture Choice
Quantization: The MP3 of AI
Context Windows: The Desk Analogy
Thinking Models: Speed vs Intelligence
Exercise: Read a Model Card
03
How Models Are Trained
The three stages of training. Why some models outperform others. What “open” actually means — and what you really get.
The Three Stages of Training
Why Some Models Are Better Than Others
Open vs Closed: What You Actually Get
Exercise: The Cost Comparison
04
Understanding Benchmarks
What benchmarks measure, how they lie, and how to actually evaluate a model for your use case.
What Benchmarks Are and Why They Exist
How Benchmarks Lie
How to Actually Evaluate Models
Exercise: Benchmark Detective
05
Running Models Yourself
Try before you buy. Software platforms for local models. Hardware reality check — what you actually need and what you don't.
Try Before You Buy
Software to Run Local Models
Hardware Reality Check
Exercise: Try an Open Model
06
Choosing The Right Model
The decision framework. Small models for local use. Large models through APIs. Our curated picks for both.
The Decision Framework
Small Models: Run Locally
Large Open Models: API Alternatives
Exercise: Find Your Model
Our Picks: Models for Your Own Computer
Our Picks: Models Through Online Services
07
The Fast Moving Landscape
How to stay current without drowning. What’s coming next. Build a learning system that outlasts any model release.
How to Stay Current
What Is Coming Next
Exercise: Your Learning System
7 modules · 30 lessons · Exercises in every module
03 // Why This Course
Practical Literacy, Not Engineering Degree
What you get here
Concepts explained through analogies, not equations
A decision framework that survives model releases
Honest trade-offs — not “open source is always better”
Hands-on exercises in every module
Curated model picks, regularly updated
Community of professionals making the same decisions
What you find elsewhere
Technical papers written for researchers
Hot takes that expire in two weeks
Hype without honest downsides
Theory with no practice
Outdated model recommendations
Reddit threads with contradicting advice
04 // Who This Is For
Is This Course Right for You?
Built for you if:
You use AI daily and want to understand what you're using
You hear “quantization” and “MoE” and want to know what they mean
You want to evaluate models yourself instead of following hype
You’re considering running AI locally for privacy or cost
You make technology decisions for yourself or your team
Not designed for:
ML engineers who train models — this is user-level, not research-level
People who want a single “best model” answer — the course teaches frameworks, not hot takes
Anyone looking for a coding bootcamp — no programming required or taught
05 // Instructor
Your Instructor
S
Sjoerd Tiemensma
Freelance developer and AI automation specialist. Writes the “Use AI” newsletter (2,000+ subscribers), certified Make.com Expert, and builds AI-powered systems for clients daily. Sjoerd turns complex AI concepts into practical, accessible explanations — and designed this course to do the same for you.
No. The course is designed for professionals who use AI, not build it. Every concept is explained through analogies and plain language. If you can install an app, you can follow this course.
Do I need special hardware to follow along?
No. Module 5 shows you how to try open models through a browser for a few dollars before investing in anything. Local hardware is optional — the course teaches you when it makes sense and when it doesn't.
Won't this be outdated in a few months?
Specific model names change. The frameworks for evaluating them don't. The course teaches you how to think about models, read model cards, evaluate benchmarks, and make decisions — skills that survive any release cycle. We also keep the curated picks updated.
How is this different from watching YouTube explainers?
YouTube gives you 10-minute overviews of single topics. This course builds a complete understanding, step by step, with exercises that make you apply what you learn to your actual work. Plus direct access to the instructor and community.
I already run local models. Is this still useful?
If you already run Ollama or LM Studio, modules 1-2 might be review. But the benchmarks deep-dive, the decision framework, and the curated picks in modules 4-6 go beyond surface-level knowledge. Most experienced users find blind spots they didn't know they had.
07 // More Courses
Continue Building
Open-source literacy is the foundation. These courses show you what to build with it.