You’ll discover a practical method to overcome AI’s lack of session memory by creating four markdown files—discovery, research, plan, and progress—that are version‑controlled, pasted at each start, and keep the project’s purpose, decisions, and status instantly available.
In this post you discover why splitting project documentation into four simple markdown files gives AI the exact context it needs, avoids repeated explanations, and makes every session start where the last one left off.
A logical project structure reduces file hunting and speeds up feature development. Follow five pillars—component‑based nesting, consistent naming, keep files under 200 lines, separate logic from UI, and thorough documentation—to keep your codebase maintainable as it grows.
In this guide you’ll discover a four‑step blueprint—from crafting a PRD and user‑flow with Claude, through generating a cohesive design system via Google Stitch, to extracting global styles and building each screen modularly—so your app feels handcrafted, not AI‑slop.
In this post you’ll learn why vague prompts cause misinterpretations and how, through targeted questions, you can create a clear brief that reduces AI hallucinations and delivers better results for your project.
You’ll learn why missing a .gitignore, outdated libraries, and no rate limiting expose your app, and get clear steps to spot these gaps, update dependencies, and instantly add protection for your users.
In this article I’ll show you why avoiding secret leaks, using established auth providers, and conducting security reviews are essential to protect AI‑generated applications from hackers, ensuring you can develop safely.
Stop wasting time guessing prompts. Get consistent, professional AI results right from the first try, every time.
