Stop Reviewing Code, Start Reviewing Tests: TDD in the Age of AI

The Shifting Role of the Developer: From Writing Code to Writing Specs

Have you ever asked an AI to build something, only to realize it built the exact wrong thing? AI is incredible at generating code, but it lacks human judgment. In this episode, I share the story of building an internal AI coaching bot called "Jugnu." My first attempt failed completely because I relied on the AI's judgment instead of doing the research myself. I quickly learned that the most successful AI developers spend 90% of their time researching, planning, and writing specs before they ever type a prompt. Join me as we explore how the role of the software engineer is merging with the product manager, the death of "un-used software," and the exact frameworks and tools (like NotebookLM and Superpowers) you can use to master AI-assisted development.

Stop Reviewing Code, Start Reviewing Tests: TDD in the Age of AI

As AI agents become capable of rewriting entire C compilers and Next.js repositories in days, the value of software is shifting away from the code itself. If LLMs write probabilistic code, how do you guarantee deterministic results? The answer: Tests. In this episode, we argue that developers need to stop agonizing over AI-generated implementation code and instead double down on writing, reviewing, and curating rigorous test suites. Learn why some open-source projects are actually closing their test repos, and how adopting an Architect mindset will unlock true AI autonomy in your workflow.

From Coder to Architect: The Power of the Producer Mindset

The role of the developer is changing. In this episode, we discuss how to stop being a "consumer" of AI prompts and start becoming a "producer" of AI systems. By shifting your focus from writing code to designing autonomous loops and persistent architectures, you can move up the ladder of value and start steering the future of software development.

The AI Autonomy Paradox: Why Software Engineers Must Unlearn "Control"

As software engineers, we are trained to control every detail—from code formatting to system architecture. But autonomous AI demands the exact opposite. In this episode, we discuss the "Autonomy Paradox." Learn why enforcing arbitrary rules on AI agents causes them to fail, and how shifting your focus from "how it's built" to "what it achieves" is the key to unlocking next-generation development.

Agentic Transformation Concept: Progressive Disclosure

If you are dumping your entire codebase into Claude or Gemini and hoping for the best, you are doing it wrong. In today's episode, we tackle Context Engineering. AI agents have short-term memory (the Goldfish Problem) and limited Context Windows. If you fill that window with unnecessary data or heavy MCPs, the AI's performance drops drastically. Tune in to learn how to structure your information using Agent files, Skill files, and Rule files. We also break down advanced strategies like Progressive Disclosure (only giving the AI what it needs, when it needs it) and using Sub-Agents to keep your main context window clean.