Field notes
Practical writing on maintainability, types, architecture, and the point where AI-assisted apps need stronger engineering foundations.
Linting Is Not Pedantry When an AI Is Writing Your Code
Why formatting, import rules, naming conventions, and static checks become practical safety systems in AI-heavy workflows.
Read articleYour App Needs a Constitution: Constraints for AI Coding Agents
A practical way to turn architectural decisions, naming conventions, data rules, and forbidden patterns into durable project constraints.
Read articleWhy Your AI Keeps Reintroducing the Same Bugs
Recurring bugs are usually signs that the codebase needs executable memory, not longer prompts.
Read articleCI/CD Is the Adult Supervision Your Vibe-Coded App Needs
How automated delivery checks turn AI coding from a risky interactive loop into a controlled path to production.
Read articleThe Difference Between a Demo and a Durable Product
Why impressive AI-generated demos still need hardening before they can survive users, edge cases, deployment, and change.
Read articleStop Asking the Agent to Refactor Everything at Once
Large AI-driven refactors usually fail because the context is too broad, the dependencies are hidden, and success is underspecified.
Read articleWhy Large Vibe-Coded Apps Become Incoherent
Why AI-built apps lose architectural shape as they grow, and how to give agents a map they can follow.
Read articleFrom Prompting to Engineering: The Maturity Curve of AI-Built Apps
A practical maturity model for moving AI-built apps from fast prompting to guarded repos, production ownership, and learning systems.
Read articleHow to Know when your Vibe-Coded App has Outgrown the Vibe
A practical scorecard for the moment when an AI-built prototype needs product maturity, delivery discipline, and a rewrite-versus-remediate plan.
Read articleTypes Are Memory for Your Codebase
How TypeScript, schemas, and explicit interfaces act as durable context for both humans and agents.
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