The practical library

Build fast. Check the work.

Evidence-first guides for testing, auditing, fixing, and launching AI-built websites and web apps.

Start with the audit and testing guides

A practical scope for reviewing a public Lovable app before launch, including evidence, severity, limitations, and next steps.

A hands-on Lovable testing workflow for preview, data, permissions, payments, failures, mobile checks, and production smoke tests.

A beginner-friendly, cross-platform method for testing an AI-built app through scenarios, observable results, and evidence.

A practical Pass, Fail, Not tested, or Not applicable checklist for deciding whether an AI-built app is ready to share.

Learn what an evidence-based AI app audit report should contain, how to read scores, and how to prioritize findings.

A task-centered website UX audit process for finding observable friction in navigation, forms, mobile use, and conversion paths.

Audit one campaign-to-action journey for message match, offer clarity, proof, forms, checkout transition, speed, and measurement.

Practical design choices that make an AI-built app clearer, more distinctive, and easier to trust.

A seven-step debugging ladder for fixing an AI-built app without replacing working parts.

A safer launch decision framework for an MVP: resolve blockers, choose the audience, and learn from evidence.

How to add payments, transactional email, and useful analytics without trusting surface-level success states.

Choose an AI app builder by workflow, control, verification needs, and the product you are building.

Kathryn Finney's historical cost experience and practical ways to reduce wasted AI-building work.

Use one core action and a deliberately small first release to reduce complexity and learn sooner.