How Did I Create QuantumTornado?
- mansour ansari
- Jul 8
- 3 min read
The journey to QuantumTornado began with a larger mission: to build a linguistic framework I call ZABAN-e-Quantum—or simply, ZABAN. This system explores how quantum entropy, symbolic logic, and physics-based modeling can merge to create a new language for machines, forecasts, and even interstellar communication.
But ZABAN needed validation—a real-world application that could prove its value. After running hundreds of simulations across fields like oil and gas, drug discovery, and financial risk analysis, I asked myself: What’s the most urgent system we still haven’t upgraded with true entropy?
The answer was clear: severe weather forecasting.
So I built QuantumTornado, a system designed to inject quantum entropy into a classically deterministic simulation—one rooted in the chaos of weather, but still governed by Newtonian rules. By feeding it randomness harvested from Quantum Random Number Generators (QRNGs) and quantum computers, I was able to reveal anomalies, early collapse zones, and lead-time advantages that classical systems either ignored or couldn't see at all.
What emerged wasn’t just a product—it was the birth of a new discipline: quantum meteorology.
What It Took to Build
To create QuantumTornado, I needed a foundation that most startups would need entire teams to manage:
A physicist's understanding of entropy, collapse theory, and quantum computing
A meteorologist’s eye for pattern recognition and field-validated storm forecasting
A pipeline to generate and distribute fresh quantum entropy, uploaded to a secure cloud bucket I manage via Google—removing the need for server maintenance while supporting global scale
A full USB QRNG farm interface—custom-built Python and batch scripts that coordinate entropy harvesting on demand —I built that entire pipeline in 90 days. It works. I have extracted and uploaded a few hundred thousand of them weekly. It breaks sometimes, but it is my pipeline! I fix and maintain it. That is sort of like my secret sauce, my business trade secrets.
All this had to be built before I even touched the user interface.
Solo Developer, Scaled Ambition
I’m a solo developer, but I built QuantumTornado as if I had a whole team:
Frontend, backend, API design, map overlays, NOAA data feeds
Subscription and licensing systems for browser-based access
Symbolic seeding tools for ZABAN's future integration with D-Wave Annealing Engines
To manage it all, I created a system of AI agents—one for each stage of the process: design, implementation, testing, documentation, and entropy harvesting. Without them, this project would’ve taken years. I am not sure if I want to spend more than a year on this. If it is profitable, I will continue; otherwise, I will stop and move on. And for the budget? That's a different post! - Oh, I am close to having a provisional patent on this!
The best AI coder for JavaScript that I use a lot and am very familiar with is the Google Gemini Pro for building the foundation. Then I utilize Loveable AI coders to create the user interface, database access, subscription system, chat, video screen recording, and build technical documentation, as well as simulation building using my Entropy bucket. I also build these simulations side by side using Python language and Streamlit technology to verify my work done using Node.js. Meanwhile, another AI helps build a manual for the product and becomes a support staff member in my startup. She knows everything about the project, including documenting code snippets, building and suggestions for phase building, documenting, and guiding through tough moments of finding answers to unknown errors that even AI could not find quickly. She also helps with blog posts and a marketing pitch. And correcting my mistakes.
Instead, I built the infrastructure, the simulations, and the browser-based forecasting engine in under 10 to 11 months.
PS: My next Quantum-seeded simulation will take half this time. I now know what not to do!
Why It Matters
QuantumTornado isn’t about pretty maps. It’s about life-saving intelligence—the ability to show a forecaster, a utility manager, or a family where to look again—even when the radar shows nothing.
Where classical systems see noise, I see signals.
This isn’t just a product. This is physics in action. This is entropy, with purpose.
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