A free course for software engineers. 6 weeks. Real projects.
Software engineers and computing students who can already write code and want to become genuinely effective working with AI. You should be comfortable with Python, Git, and a terminal.
This is not an intro to programming course. It's not an ML theory course. It's a practical course about building real things with AI tools, understanding when to use them, and knowing when not to.
| Week | Theme | You'll Build |
|---|---|---|
| 0 | Launch Pad | Environment setup, first API call |
| 1 | Thinking With AI | AI collaboration charter, failure analysis |
| 2 | AI-Augmented Coding | Before/after case study on a real codebase |
| 3 | AI-Powered Features | Web app with an AI feature, deployed live |
| 4 | RAG & Knowledge Systems | Q&A system over a custom knowledge base |
| 5 | Agents & Automation | An agent that automates something in your life |
| 6 | Portfolio & What's Next | Polished portfolio, reflective write-up |
Start with Week 0: Launch Pad. It walks you through everything.
Quick version:
- Create accounts: GitHub, Anthropic, Railway, Anseo
- Open this repo in GitHub Codespaces
- Run
python verify.py - Join the course community on Anseo
- 5 sessions per week (30–45 min each): one concept, one exercise, one output. Work through them at your own pace — binge them Tuesday evening or spread them across the week. Sequence matters; timing doesn't.
- Weekly project: build something real, deploy it, share it. Due each Sunday — this is when the cohort syncs up for peer review.
- Community: peer review, shared builds, collaborative learning on Anseo
- ~5 hours/week, flexible when. Two evenings and a Saturday morning. Or one long Sunday. Whatever fits your life.
- No grades, no certificates: you walk away with a portfolio of deployed projects and the skills to back them up
- Catch-up friendly: miss a week because of exams or deadlines? Rejoin the next one. The community posts and peer projects are there for you to get context on what you missed.
A small tool to manage your workflow. It's installed automatically when you open the Codespace. If you're running locally, install it with pip install -e tools/.
bwai check-env # verify your setup
bwai new-project NAME # scaffold a weekly project
bwai api-test # test your API connection
bwai token-count TEXT # count tokens in text or a file
bwai submit # push and share your projectThis course is built on a simple idea: AI should make engineers more capable, not less.
We don't teach you to outsource your thinking to a chatbot. We teach you to use AI as a powerful collaborator while maintaining — and strengthening — your engineering judgment.
The curriculum is informed by cyberpsychology research on how humans actually learn and interact with technology. Every design decision, from session length to peer review structures, is intentional.
- Anseo — community platform
- Anthropic Claude — AI API
- GitHub Codespaces — development environment
- Railway — deployment
Course content is released under CC BY-SA 4.0. Code is released under MIT.
Use it. Fork it. Teach it. Make it better.
Created by Todd McCaffrey as part of the Anseo project.