How I build amazing apps with AI

You can check it here:https://zabanpeyma.com/

As of today, Google's Gemini AI stands out as the best AI-powered code generator. If you have a brilliant idea, you can bring it to life, but it’s not always easy. Before AI coding tools, you had to take on the role of a project manager, coordinating multiple developers with specialized skills: one for databases and data manipulation, another for backend development, a specialist in traffic management, someone to build proprietary tools for continuous workflow, and finally, an expert in user-friendly frontend design. And if your vision included mobile compatibility, careful planning was essential.

Now, think like a project manager again—but this time with an AI coder at your side. AI developers are "almost" as skilled as human coders, albeit 100 times faster and capable of forward-thinking logic. They don't take breaks and don't get sick often. However, they’re not always perfect. You might need to test and refine parts of your project through multiple iterations, referencing errors, troubleshooting, and refining your approach. You must immerse yourself in this process, investing countless hours into testing, refining, and improving your work. Progress may feel slow, intense, and sometimes overwhelming. But compared to traditional human teams, months of development can now be condensed into weeks—or in some cases, even hours.

Depending on your project's complexity, AI can save enormous amounts of time and money. The key to success lies in clear communication, meticulous planning, thorough documentation, and continuous testing.

For those who understand the landscape, Gemini AI is the best option. ChatGPT is a close competitor, but the edge goes to Google’s AI. Ultimately, the real advantage goes to the human user who can effectively communicate with the AI.

ChatGPT excels in Python scripting, and since Python is incredibly versatile, you can accomplish almost anything with it. However, if you need additional flexibility, Node.js  is ideal for managing complex mathematical, physics, and scientific projects. Oh, before I forget, Claud 4 is also excellent, but too many mistakes, and it can be an intense process.

Beyond coding, ChatGPT is great at documenting your code, outlining development roadmaps, and even creating marketing and sales strategies for your project. You can use it to build intelligent agents that manage emails, search your PC, schedule tasks, and more. But these functions, while useful, aren’t what truly impresses me. What’s truly groundbreaking is the ability to ask an AI coder to build virtually any app, sparking an explosion of creativity.

Here’s an example:

Over the past few months, I designed a pipeline to manage data input and output workflows using Google Buckets. No AI can build such a system in a single attempt—you still need to construct the pipeline manually. AI can generate Python scripts to assist in building the scaffolding, but ultimately, you must handle around 80% of the workload yourself.

The core idea was to integrate this pipeline into all my projects. One particularly unique aspect is its ability to fetch Quantum Entropy data from local hardware, then pipeline that data to web applications for use in cryptography, large dataset simulations, and other applications requiring pure randomness. The hardware access is all in Python, and I designed and built my home-grown Entropy Data Capture from a USB Quantum Optical Module made by Cryptalabs. A cool Quantum Device used for Entropy Injection into Cryptography applications. Unlike classical random number generators, which are predictable and vulnerable to hacking, quantum entropy provides a truly unpredictable source.

Building on this foundation, I’ve developed Monte Carlo simulations powered by fresh quantum entropy keys, allowing comparisons between classical and quantum entropy injection—an enormous leap forward in large-scale simulations.

So far, I’ve created simulations for oil and gas, wealth analysis, drug discovery, and more. Both OpenAI’s tools and Google’s AI have been invaluable in developing these projects using Python and Streamlit. Be prepared—each app can take 30 to 50 hours to complete, incorporating features like optional local entropy fetching from PCI or USB devices, multi-tab interfaces, data import/export functionalities, encryption, documentation, and code management, all of which take time.

Every project I build is just one piece of a larger vision—a Quantum Linguistic Framework derived from Quantum Entropy hardware. Phase One involved developing the pipeline to produce vast entropy datasets for training AI models such as Convolutional Neural Networks (CNN) or Principal Component Analysis (PCA). This phase prepares AI scripts for Phase Two, which will leverage Quantum Annealing (energy landscape optimization) and Quantum Ion Trapping systems from D-Wave and IonQ. These advanced datasets will enable AI to recognize symbolic patterns emerging from quantum entropy injection, forming the basis of an AI-to-AI communication model. My goal? Creating an interstellar communication system rooted in quantum entanglement, paving the way for the Quantum Linguistic Framework I call Zaba-e-Quantum, or Zaban.

AI has played a crucial role in the design and implementation of this project, though the foundational concepts stem from original thought experiments inspired by quantum physics—particularly the mathematics of quantum entanglement and tunneling. There’s far more to entanglement measurements than cryptographic applications. Each collapse of a wave function carries a unique signature from the cosmos itself.

Soon, I’ll build a prototype of this system—my own version of a "Hello World" from the quantum realm.

if interested in my work, my projects, drop me a note:

videomover@gmail.com

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PS: I asked AI to provide me with the hashtags for this content. That request was granted, saving me 5 minutes of typing and thinking about hashtags.

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