There’s a moment when you realize you haven’t typed a single line of code in 45 minutes, and your app is somehow better than anything you would have written on your own. That moment is the point. This is the tutorial I wish I’d had before I started.
Step 0: Clear Requirements First
The biggest mistake beginners make is opening an AI editor and typing “build me a task app.” The AI will build you something. It just won’t be what you actually want.
Before you write a single prompt, spend 20 minutes with a blank document answering these questions: Who uses this? What’s the one thing they need to do? What should it explicitly not do? What does success look like in one sentence?
For this tutorial, the answers were: personal use, capture and prioritize tasks, no team features, success means I actually use it instead of going back to sticky notes.
That document becomes your first prompt.
Step 1: The First Prompt
Don’t ask for features. Describe intent.
Wrong: “Build a task management app with a list view, priority levels, due dates, and tags.”
Right: “I need a personal task manager. The core problem is that my sticky notes system breaks down when I have more than 10 things. I need to capture tasks quickly, see what matters most today, and mark things done without friction. I don’t need teams, comments, or attachments.”
The first version you get back will be rough. That’s fine. You’re not looking for finished code — you’re looking for a foundation that understands the problem.
Step 2: The Iteration Loop
Here’s the discipline that separates vibe coding that ships from vibe coding that stalls: be specific about what, never about how.
Good iteration: “The task input field doesn’t clear after I press Enter. It should clear and keep focus so I can add another task immediately.”
Bad iteration: “Fix the task input. Also, can you use useState with a useEffect that resets the value…”
The moment you start telling the AI how to fix something, you’ve taken on cognitive load that the AI should be carrying. Trust it to know how. Your job is to know what’s wrong and what better looks like.
I ran about 30 iterations over three hours. Most took under 2 minutes each. The loop felt like pair programming with someone who can type 500 words per minute and never gets frustrated.
Step 3: What Went Wrong
Be honest with yourself here, because things will go wrong and the failures are instructive.
Security. The first version stored everything in localStorage and had no input sanitization. I caught this because I thought to ask, not because the AI flagged it unprompted. Lesson: always ask “what did you assume about security here?” after any data-handling code.
Timezones. Due dates were stored in UTC but displayed without conversion. Tasks due “today” would flip to “tomorrow” at 7pm local time. This was invisible in development and would have been maddening in production. I only caught it because I manually tested at 8pm.
Scope creep. Around hour two, I started asking for features outside my original spec — recurring tasks, subtasks, a calendar view. Each one made the app worse, not better. The original discipline of requirements exists precisely for this moment.
Step 4: The Result
What shipped: a task app with a capture box, a priority toggle, a “today” filter, and one-click completion. Under 400 lines of code. Deployed in about 8 minutes.
Is it the best task app ever built? No. Is it better than my sticky notes system? Yes. Does it solve the exact problem I defined? Exactly.
The Takeaway
Vibe coding isn’t about removing the human from software development. It’s about changing what the human is responsible for. You still need taste — the ability to know that clearing the input field matters, that timezone bugs are real, that scope creep kills focus.
You need judgment. You need to know when to iterate and when to ship. You need to notice when the AI is solving the wrong problem with great confidence.
What you don’t need is to hold the implementation details in your head. That’s the deal. Give up the how, keep the what and the why. Build things that actually solve problems.
The best developers I’ve seen adapt to this quickly, not because they stop caring about code quality, but because they realize their real value was never in the typing.