Jason Gorman, a software development expert and founder of Codemanship, joins host Giovanni Asproni to explore how best to use AI in software development. They start by considering how established technical practices — test-driven development, modularization, continuous integration, and continuous reviews — become more important, not less, when working with AI assistance. These practices help address several key limitations of LLMs, including keeping context windows as small as possible.
Looking at AI’s impact on team productivity, Jason offers some practical advice for teams to introduce AI tools into their workflows.
The episode also explores spec-driven development, agentic programming, and the importance of writing readable, understandable code — even when it’s AI-generated. Finally, Jason and Giovanni look at emerging research into the cognitive downsides of over-reliance on AI, and what developers can do about it.
Brought to you by IEEE Computer Society and IEEE Software magazine.
Show Notes
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Related Resources
- The AI-Ready Software Developer – Index
- CRESS Principles for Context Engineering
- Book: Process Over Magic: Beyond Vibe Coding by Uberto Barbini
- What Is Agentic Coding?
- Specification-Driven Development (SDD)
- Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
- Ralph Loops
- Kent Beck Canon Test Driven Development
- Book: “Test-Driven Development: By Example”, Kent Beck
- Modularity
- Gas Town
- Dora Metrics
- Article: Super-intelligence or Superstition? Exploring Psychological Factors Influencing Belief in AI Predictions about Personal Behavior
- Stack Overflow
- Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
- AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
- How Does Naming Affect LLMs on Code Analysis Tasks?
