A paid programming course sells you structure and motivation — the material itself can almost always be assembled for free from open sources. Below are six repositories that cover the path from "learning a language from scratch" to "looking at how the system prompts of production tools like Cursor and Claude Code are built" — with nothing to pay for access.
1. project-based-learning — learn a language by building a real project
github.com/practical-tutorials/project-based-learning — a collection of learning projects in C, Python, JavaScript, Go, Rust and other languages. Every item is not theory but a concrete practical task: write your own text editor, database, web server, interpreter.
How to use it: pick the language you're learning, find a project at your level in the table of contents and follow the tutorial step by step — most of them link to external guides with a full code walkthrough rather than just describing the task.
2. ml-for-beginners — a 12-week ML course from Microsoft
github.com/microsoft/ml-for-beginners is a free course with lessons, quizzes and projects in Python and Scikit-learn. It's aimed at people starting from zero — no diving into deep learning, only classic machine learning algorithms.
How to use it: the course is structured by week with a lesson table (number, topic, notebook, author) — you work through it in order, and each lesson closes with a practical assignment on real data.
3. 30-days-of-python — Python from scratch in 30 days
github.com/asabeneh/30-days-of-python takes you from syntax basics to working with APIs, databases and web frameworks. Each day brings theory, code examples and exercises to practice on your own.
How to use it: a good fit for people who have never programmed at all — the course assumes no prior knowledge and introduces concepts gradually, day by day, without skipping the basics.
4. build-your-own-x — build Git, Docker or your own OS from scratch
github.com/codecrafters-io/build-your-own-x — a collection of guides on writing your own versions of popular technologies: Git, Docker, Redis, databases, programming languages and much more. The idea is to understand how a tool works by implementing it yourself instead of just reading the documentation.
How to use it: pick the technology you want to understand more deeply, find the matching guide in the list of categories (3D Renderer, AI Model, Augmented Reality and others) and work through it step by step in a language of your choosing.
5. 500+ AI/ML/DL/CV/NLP Projects with Code — a ready portfolio of examples
github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code — a collection of 500+ ready open-source implementations of machine learning, deep learning, computer vision and NLP projects.
How to use it: it's not a course with a sequential syllabus but a reference — you look for a category close to your task, open a specific repository from the list and study the implementation: architecture, data preparation, inference.
6. system-prompts-and-models-of-ai-tools — an inside look at production AI
github.com/x1xhlol/system-prompts-and-models-of-ai-tools collects leaked system prompts from popular AI tools — Cursor, Claude Code, Devin, Lovable, Replit, v0 and dozens of others. You can see exactly how these products phrase instructions for their models: what constraints they set, how they describe tools, how they structure context.
How to use it: read it not to copy (the prompts are heavily tied to each tool's specific architecture) but to understand the techniques — how production systems define the model's role, set the response format and describe the available tools. That's exactly what you need when writing your own system prompts for agents.
What order to go through this collection in
If you don't have a language yet — start with 30-days-of-python or a similar course for your language. If you already have the basic syntax but want to understand "how it works under the hood" — build-your-own-x and project-based-learning give you exactly that through practice. ml-for-beginners and the AI project portfolio are a parallel track for anyone expanding into machine learning. The tools' system prompts aren't beginner material — they're for someone already writing their own prompts and agents who wants to see how it's done in production.
How we do it
In the qvib.pro knowledge base, the cards on system prompts and prompt engineering break down the techniques from such leaks in applied terms — what in the prompt structure of Cursor and Claude Code is already usable in your own agents and skills. The qvib engine is a working example of a product that grew out of the practice of "build it yourself and figure it out", not just out of watching courses.
Related links
- Free programming resources: qvib.pro/arsenal/vibe-coding/besplatnye-resursy-po-programmirovaniu/
- System prompts of AI tools: qvib.pro/arsenal/prompts/sistemnye-promty-ai-instrumentov/
- How the qvib engine works: qvib.pro/