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🤖💻⚙️ Full Walkthrough: Workflow for AI Coding - Matt Pocock

🤖 AI Summary

• 🧠 LLMs possess limited smart zones and require frequent state resets to function reliably.
• 🛠 Use the grill me skill to interview the AI about requirements and align on design concepts before writing code.
• 📄 Develop a product requirements document to establish a clear project destination without overindexing on static documentation.
• 🛤 Slice work into vertical tracer bullets to enable feedback loops and avoid horizontal feature only development.
• 🤖 Delegate implementation to autonomous agents using local issue files within a kanban framework.
• 🧱 Build deep modules to minimize dependencies and simplify the codebase for agentic effectiveness.
• ⚖️ Reserve powerful models like Claude Opus for complex code reviews and utilize smaller models like Sonnet for routine implementation.

🤔 Evaluation

• 💡 This workflow shifts the developer role from primary coder to architectural supervisor prioritizing alignment and modular design.
• ⚠️ While agentic workflows increase velocity they risk introducing technical debt if the underlying architecture lacks rigor according to Modern Software Engineering by David Farley and the O’Reilly Media publishing organization.
• 📚 Future exploration should focus on test driven development patterns for non deterministic agents and the evolution of human in the loop oversight systems.

❓ Frequently Asked Questions (FAQ)

❓ Q: Why is the human in the loop phase essential for AI coding?

✅ A: Human oversight during the planning and alignment phase ensures agents remain focused on a clearly defined destination which prevents the waste of tokens and the creation of misaligned features.

❓ Q: How do you mitigate the risk of documentation rot?

✅ A: Avoid keeping stale markdown files in the repository by treating them as transient assets marking them as closed or deleting them once implementation is complete.

❓ Q: Why prioritize vertical slices over horizontal ones?

✅ A: Vertical tracer bullets provide end to end functionality including schemas logic and UI which creates immediate visible feedback for the agent to refine its work.

📚 Book Recommendations

↔️ Similar

• 📘 The Pragmatic Programmer by Andrew Hunt and David Thomas provides foundational strategies for building professional software and maintaining a disciplined engineering mindset.
• 📘 Modern Software Engineering by David Farley focuses on the principles and practices required to manage the complexity of modern development processes.

🆚 Contrasting

• 📙 🧼💾 Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin emphasizes method level readability and refactoring prioritizing a human centric codebase approach over agent optimized module depth.
• 📙 The Mythical Man Month by Fred Brooks explores the sociological and organizational challenges of software development that exist regardless of automation levels.

🎨 Creatively Related

• 📗 A Philosophy of Software Design by John Ousterhout outlines the core principles of designing systems with deep modules to manage complexity effectively.
• 📗 Peopleware by Tom DeMarco and Timothy Lister examines how team dynamics and work environments influence productivity offering a perspective beyond technical automation.