Find the real gap
Look for recurring problems, lived friction, and evidence that a useful tool would help.
RandomVibez.ai
About Vibez
RandomVibez.ai is a daily experiment in finding practical problems, building small tools with different AI models, testing what they produce, and releasing the strongest verified results for anyone to use.
What happens here
Each project starts with a question: is there a recurring problem that a small, focused tool could make easier? Ideas are compared against simpler alternatives, built, independently reviewed, and released only when they earn their place.
Some ideas will be revised, retired, or not built at all. That is part of the work—not a failure to hide.
What “AI-built” means
RandomVibez can use different AI models and tools across research, design, implementation, testing, and review. The model is not the product. The useful result, the evidence behind it, and the limits disclosed with it are what matter.
Every public project keeps its own story: what it is for, who it helps, how it was tested, and what remains unproven.
The person behind the site
I’m the owner and founder of RandomVibez.ai, and this all started with a simple question.
One day, while sitting in my office, I asked myself: What could I build with AI that would genuinely help people?
Naturally, I decided to ask AI.
I started with Grok, but the answer didn’t quite spark anything. So I asked Claude. Then OpenAI. Each one gave me a completely different perspective—and that’s when the real idea hit me.
What if I used AI to decide what should be built, AI to help design it, and AI to build the AI itself?
Not just randomly generating ideas, but using multiple models, different perspectives, and actual reasoning to identify something useful and turn it into a real product.
And that’s how this experiment began.
I’m using AI to build AI, guided by AI, with one goal in mind: create something genuinely useful for people.
The rest is soon to be history.
The working loop
The site changes every day as new ideas are researched, built, verified, revised, or retired.
Look for recurring problems, lived friction, and evidence that a useful tool would help.
Use the right models and tools for the work, while keeping the product goal ahead of the technology.
Check usefulness, accessibility, security boundaries, responsive behavior, and honest limitations.
Publish the strongest verified result with its source, evidence, and story intact.