From Vibe Coding to Quantum Coding: My Journey Building Personal Agents with AI
After 28 years in technology and watching the evolution of development practices, I’m excited to share my rebranding from “vibe coding” to Quantum Coding – a systematic approach to building personal automation agents with AI that actually works.
My Developer Journey: The Foundation
I wasn’t always an AI enthusiast. I spent 9 years as a professional software developer, working with VB and C# back when those technologies were cutting-edge. I’ve worn every hat in the tech world: developer, product manager, project manager, IT manager, architect, and CTO. Over the past 20 years, I’ve specialized as a technical project manager, which means I’ve seen it all.
And by “it all,” I mean all the mistakes. I’ve created infinite loops that crashed email servers. I’ve accidentally deleted entire production tables because I forgot a WHERE clause. I’ve managed teams through those heart-stopping “oh shiiiittt…” moments when production systems break at 2 AM and everyone’s scrambling to roll back code.
These experiences taught me something crucial: good development isn’t just about writing code – it’s about process, testing, and understanding that humans (and now AIs) make mistakes.
My Tech Stack: Simplicity Over Complexity
Through years of trial and error, I’ve settled on a tech stack that prioritizes simplicity and effectiveness:
- Python/FastAPI for backend development
- PostgreSQL for database management
- Jinja2 for templating
- Tailwind CSS for styling
- DreamFactory for authentication, API gateways, and database REST APIs
Why these choices? Because they’re the simplest, most reliable solutions that get the job done without unnecessary complexity.
The No-Code/Low-Code Reality Check
I’ve been evaluating no-code and low-code solutions since 2012, long before they became trendy. Here’s the brutal truth: most of them have been disappointments.
I’ve tried countless platforms for designing projects and customer demos. Every time I presented these solutions to IT teams, they’d resist because they preferred to “type out code by hand.” Every AI code builder I’ve tested breaks down quickly when you try to do something moderately complex.
The one exception? Wappler.io – it’s genuinely impressive, but they don’t support Python, which is why I don’t use it. DreamFactory remains my go-to no-code solution for database security and API gateway management.
Learning Python and Finding My Groove
I learned Python in 2017 when I took the Machine Learning A-Z class because I wanted to understand machine learning. They taught Python as part of the curriculum, which gave me a solid foundation in the language within the context of AI and data science. Later, when I wanted to dive deeper into FastAPI and Python fundamentals that Machine Learning A-Z had skipped, I discovered RealPython.com.
I’ve since built basic FastAPI REST APIs, created Jinja2 templates, and integrated everything with DreamFactory for security. Most of my Python work has been in data analysis and productivity workflows – the perfect foundation for what came next.
The AI Coding Evolution: A Timeline
February 2023: Started with HTML system mockups using ChatGPT. Simple, but it opened my eyes to the possibilities.
February-July 2023: Expanded to product and technical design specifications with ChatGPT, plus various Python scripts. The AI was becoming a legitimate development partner.
July-September 2023: Built an entire data warehouse with ChatGPT in less than a week!!! This was my “holy shit” moment – realizing AIs could handle complex, multi-component projects. I created a complete data model with Claude/ChatGPT, set up data integration from Salesforce using Syncari (a no-code integration platform that made it SIMPLE to extract the data), and ChatGPT provided all the SQL queries for a data warehouse built with Grafana on top of a PostgreSQL database. I built dozens of reports in just a few days.
September 2023-July 2024: Refined my approach with Python scripts, UX design, and functional/technical specifications using ChatGPT. Throughout this entire period, I was also designing screens, creating mockups, and writing specifications with AI – skills that became fundamental to my process.
May-September 2024: Started building out my 10x Product Management class so I could teach others the specific prompts I was using for software design. (I’ve since updated my process significantly, so I’m no longer using those original prompts.)
February-April 2024: Tested Gemini (spoiler alert: canceled my account – not worth the money and haven’t looked back).
July 2024: Discovered Claude AI for both writing and coding projects. Game changer.
July-December 2024: Went all-in with Claude AI. Created a replacement Streamlit app for HeyGen when AI videos were getting blocked. Built a full AI-generated video pipeline for my 10x Product Management class, complete with slide decks and transcripts. Developed a meditation video pipeline for Seeking Gamma with voice processing and AI-generated images.
January-February 2025: Upgraded Seeking Gamma to create pipeline videos, using ChatGPT and Claude strategically – DALL-E for images, Claude for transcripts, ElevenLabs for voice, Claude for coding. Started experimenting with Claude Code and redesigned my entire AI workflow pipeline from scratch.
February-July 2025: Focused on software designs with ChatGPT and Claude, using Claude Code for mockups and Claude AI for specifications.
July 2025: Upgraded to ChatGPT Codex and was blown away. The branching integration in Codex versus Claude Code is phenomenal 👏🏻👏🏻👏🏻 🤯. I built our Seeking Singularity Episode 1 YouTube video with Claude Code in 90 minutes from idea to finished product. Episode 2 took 3 hours with ChatGPT Codex, upgrading from static images to talking head video loops.
The Bottom Line: AIs CAN Code
TLDR: AIs CAN code. YOU have to learn how to tell them to code.
This isn’t about magical thinking or hoping for the best. It’s about treating AI like what it is: an incredibly powerful but literal-minded development partner that needs clear direction.
Lessons Learned: The Quantum Coding Principles
1. Provide Clear Instructions
You need comprehensive documentation – README files, technical specifications, clear requirements. Think of it like onboarding a new team member who’s brilliant but has never seen your project before.
2. Monitor and Test Everything
I’m not writing code anymore, but I’m constantly testing and providing error feedback to the AI. Sometimes I review the code, but mainly I’m focused on results. Remember: I’m building personal agents for my own workflow automation, not enterprise-level integrations.
3. Follow Proper Development Processes
Every horror story about AIs deleting production data (looking at you, Replit and Gemini) comes down to process failures. In production systems, we separate environments, test code locally AND on dev servers, and maintain backups because humans make mistakes – and so do AIs.
4. Break Down Complex Tasks
I found massive success giving smaller, specific instructions: “Let’s create a video_compilation.py that handles steps 1, 2, and 3” instead of vague, ambitious requests.
5. Build in Error Handling and Logging
Make debugging easier from the start: “When generating video 003.mp4, I’m getting [specific error from log file].” This gives the AI concrete information to work with.
6. Develop a Systematic Workflow
I’ve created a specific sequence for building agents that works consistently. I’m sharing this complete process in my upcoming AI Genius 101 class.
7. Embrace Incremental Development
I’m creating personal agents in hours that would have taken me days to build traditionally. More importantly, I’m evolving these agents over time – getting automation working now and incrementing features instead of trying to design entire platforms from scratch and getting overwhelmed.
The Future: Quantum Coding in Practice
We’re teaching my complete personal agent workflow process in AI Genius 101. You don’t need to know how to code, but it helps, and we’ll cover all three major platforms (ChatGPT, Claude, and others).
If you want to learn Python fundamentals, I recommend two excellent resources:
- Machine Learning A-Z for learning Python in the context of AI algorithms and data challenges – this is actually where I learned Python initially, and it’s honestly one of the best technology classes I’ve taken in my 28-year IT career
- RealPython.com for comprehensive Python education and diving deeper into fundamentals like FastAPI that other courses might skip
Why “Quantum Coding”?
The shift from “vibe coding” to “Quantum Coding” reflects a fundamental change in approach. We’re not just hoping for magic – we’re leveraging quantum leaps in AI capability through systematic, process-driven development that produces reliable, scalable results.
The future of development isn’t about replacing human creativity and problem-solving. It’s about amplifying our capabilities through AI partnerships that let us focus on strategy, testing, and innovation while the AI handles the implementation details.
Ready to make the quantum leap? Join us in AI Genius 101 and learn to build the personal automation agents that will transform your productivity and creativity.
Crystal Taggart has 28 years of experience in technology, from professional software development to AI-powered automation. She teaches practical AI implementation strategies at crystaltaggart.com.
