Educational engineering projects designed to explore, explain, and teach technical concepts.
Every project in this collection is built so that someone can learn the topic from it — and use it as a starting point for their own work.
Part of Syntran Labs
Learning Lab is a curated catalog of self-contained educational engineering repositories from Syntran Labs. Each project lives in its own standalone learn-* repository with its own README, docs, tests, issues, and contribution path. Each project is built around a specific topic in modern software engineering, and designed to answer one question:
"If someone wanted to learn this topic by example, what would the ideal project look like?"
That means every project in this collection aims to be:
| Quality | What It Means for You as a Learner |
|---|---|
| 📖 Fully documented | Not just code — concepts, decisions, and reasoning are explained |
| 🔁 Reproducible | Clone it, run it, and get the same results, step by step |
| 🧱 Professionally structured | Learn the topic and what a well-organized project looks like |
| 🚀 A starting point | Designed to be extended — tutorials show you how to build on top |
| 🪞 Honest about scope | Educational examples, clearly labeled — no pretending to be production services |
Each project is a standalone repository with its own README, documentation, tests, issues, and contribution path.
learn-spec-driven-dev is the first published project in the Syntran Labs Learning Lab catalog.
| Project | Topic | Status | What You'll Learn |
|---|---|---|---|
| Learn Spec-Driven Development | Spec-Driven Development | ✅ Published | A hands-on Python learning project demonstrating executable specifications (OpenSpec), pytest, Red-Green-Refactor, dependency injection, responsible AI-assisted engineering |
| Databricks Primer | Data Engineering | 🔜 Coming soon | Databricks workspace fundamentals, PySpark basics, Delta Lake, notebooks and jobs — from zero to a working data pipeline |
| Neural Networks Primer | Machine Learning | 🔜 Coming soon | How neural networks actually work: perceptrons, forward pass, backpropagation, and training — built from scratch before using a framework |
🌱 New learning projects are curated and added regularly — each one designed to take you from zero to hands-on with a topic.
Code is not enough. A good learning project should explain what it does, why it exists, how it works, and how it can be validated.
For that reason, every project in this collection includes more than source code:
Functional code Technical specifications Design notes
Test cases Architecture decisions Step-by-step tutorials
Lessons learned
And follows a consistent structure, so once you've learned one project, you can navigate them all:
project/
├── README.md ← start here: what, why, how
├── specs/ ← requirements and expected behavior
├── src/ ← the implementation
├── tests/ ← how correctness is validated
└── docs/ ← guides, tutorials, and deep dives
- Pick a topic from the Published Projects table above that interests you
- Go to the project repository — each has its own complete README, documentation, and setup instructions
- Clone it and run the tests — see it working on your machine in 5 minutes
- Read the docs and follow tutorials — each project includes guides, examples, and hands-on exercises
- Contribute or extend — each project welcomes documentation, specs, tests, examples, and educational improvements
- Use as a starting point — fork it and adapt it for your own learning or teaching
- Syntran Labs — The full portfolio
- systems-lab — Production-oriented AI systems track
- paper-lab — Research-to-code track
Built at Syntran Labs by Leonardo Sigales
Found a project useful for learning? A ⭐ helps others find it too.