10 Mistakes New Vibe Coders Make (and What to Do Instead)
Most vibe coding failures aren't exotic. Newcomers hit the same ten walls, in roughly the same order. Knowing them in advance is the difference between a rough week and a smooth one.
1. Reading the code instead of running it
Generated code looks right with remarkable confidence. The only trustworthy evidence is execution. Run everything, early, on your own machine.
2. Asking for the whole app in one prompt
One giant prompt produces one giant, unreviewable blob. Ask for one coherent piece at a time; keep what works, regenerate what doesn't.
3. Prompting without context
The model can't see your stack, your files, or your conventions unless you provide them. Vague briefs produce generic code that fits nothing.
4. No version control — or no backups at all
Regeneration is destructive: the new version replaces the old, including the parts that worked. Commit (or at minimum copy) before every major regeneration, so "undo" is always possible.
5. Feeding the same error back forever
If two rounds of "here's the error, fix it" haven't worked, the approach is probably wrong. Start a fresh generation with a better brief instead of patching a doomed one.
6. Trusting the demo data
Apps arrive pre-seeded with tidy examples that make everything look finished. Delete the seed data and try it empty — the empty state is usually where it breaks first.
7. Ignoring the console
The browser console is where the app confesses. Errors and warnings pile up silently while the page still looks fine. Keep it open while you test; investigate anything red.
8. Shipping placeholders
Lorem ipsum, bracketed placeholder names, dead "#" links, example.com URLs. Do a project-wide search for these before anything goes public.
9. Letting the AI choose every dependency
Models add libraries freely — sometimes heavy ones, occasionally imaginary ones. Read your import list; every package is code you now ship and maintain.
10. Never reading any of the code
You don't need to write it, but you do need a working sense of what's there. Ask the AI to explain any file you can't summarize — and verify the explanation matches what the app actually does when it runs.
Every one of these mistakes is recoverable. The pattern behind the fixes: stay in the loop, keep the pieces small, and trust behavior over appearance.
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