Stanford's CS146S: The Modern Software Developer
I came across the Stanford’s CS146S course, The Modern Software Developer, which started this week. Taught by Mihail Eric, the course aims to equip students with the skills to master modern AI development tools and workflows. The curriculum is designed to explore how AI-assisted coding, automated testing, and emerging trends can significantly boost developer productivity: very interesting perspective for a software engineering course!
The introductory email from the course instructor outlined several key takeaways that frame the entire course. These principles move beyond simply using AI tools and focus on the evolving role of the developer.
Core Principles of Modern Development
The course emphasises a shift in mindset and practice for software engineers.
Human-agent engineering, not vibe coding. Pure vibe coding is still not a viable way to build production software. Instead, every developer must learn to become a manager of an eager set of AI agent interns.
This is a spot on reframing. The developer’s role transitions from a sole coder to a manager who provides high-level direction, system architecture, and crucial business context to AI agents.
LLMs are only as good as you are. If a developer says AI “doesn’t work on their codebase”, it typically means even a human entering their codebase for the first time would be confused. The key is to set your agents up for success with clear context and well-structured code.
This principle places the responsibility for effective AI collaboration squarely on the developer’s shoulders. It suggests that the quality of the output is directly correlated with the clarity of the input and the health of the underlying system.
Course Structure
The course schedule provides a comprehensive look at the topics that are essential for the modern developer. The ten-week curriculum covers:
| Week | Focus Area | Key Topics |
|---|---|---|
| 1-2 | AI Fundamentals | LLM mechanics, prompt engineering, agent architecture (MCP) |
| 3-5 | Development Environment | AI IDEs, terminal automation, context management |
| 6-7 | Quality & Security | AI testing, vulnerability detection, debugging, code review |
| 8-9 | Deployment & Operations | Automated UI building, monitoring, incident response |
| 10 | Future Outlook | The evolving role of the software engineer |
The inclusion of guest speakers from companies like Cognition (Russell Kaplan), Warp (Zach Lloyd), and a16z (Martin Casado) indicates a strong connection to current industry practices and future trends.
This course appears to be a valuable resource for understanding the practical application of AI in software development. I plan to follow the released materials to see how these principles are put into practice.
