Learning to build AI together. One code review at a time. Humans in the loop.
Welcome to the home of Code Club, a hands-on program for learning how to build AI by working through real code with other practitioners.
Think of Code Club as a book club for code reviews. Instead of reading the same book, participants build from the same public repository and then come together to compare implementations, explain decisions, and explore the tradeoffs behind different approaches.
It combines the energy of a virtual hackathon with the reflection of a collaborative code review. Unlike a class, there is no single "golden path" to reproduce. Unlike a competition, there is no winning project. The goal is reusable engineering knowledge: what worked, what did not, why, and what you would try next.
- Learn: Start with a public repository built around a practical AI engineering problem.
- Build: Choose a guided path or take on a deeper challenge, depending on your experience and interests.
- Review: Walk through the code together and discuss what each approach makes possible, difficult, or risky.
- Share: Capture useful techniques, lessons, and open questions so others can build on them.
- Contribute: "Fork It Forward" by improving the project or carrying what you learned back to the open-source community.
Code Club runs as a quarterly virtual session with about three hours of hands-on building followed by a collaborative review. The conversation continues in community discussion forums between events.
Code Club is for engineers, architects, pro-code developers, and AI/ML practitioners who want hands-on experience building AI solutions. Different experience levels are welcome: newer builders can follow a guided path, while experienced practitioners can explore deeper challenges and alternative designs.
Open to anyone who plans to code. No spectators.
Finishing every exercise is not the measure of success. Progress, explainable decisions, useful questions, and insights that others can reuse all count. Coaches support that process by asking questions and surfacing tradeoffs rather than simply giving away answers.
| Topic | Repository |
|---|---|
| Agent observability and optimization | Observability |
| Trustworthy AI and red teaming | Trustworthy AI |
| Local AI development | Foundry Local |
- Hands-on AI readiness — improve practical coding skills through real-world AI repositories and labs.
- Knowledge sharing — share techniques, lessons learned, and best practices across the AI & ML community.
- Open-source contribution — learn from and contribute back to open-source projects.
- Community building — form friendships, support systems, and stronger networks across teams.
- Ongoing engagement — keep discussions, troubleshooting, and learning going through discussion forums between events.
We appreciate your interest in contributing to this project! Please refer to the CONTRIBUTING page for detailed guidelines on how to contribute, including information about the Contributor License Agreement (CLA), code of conduct, and the process for submitting pull requests.
Thank you for your support and contributions!
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