Originally published on AIdeazz — cross-posted here with canonical link.
I spent $12,000 on Oracle Cloud infrastructure in the first 6 months of building AIdeazz, with zero VC funding. 45% of that budget went to experimenting with multi-agent systems, which I believed would be the key to creating autonomous AI agents. However, I soon realized that my executive experience as Deputy CEO at a Russian digital infrastructure program was both a blessing and a curse in this new endeavor.
Transferring Executive Experience
My background in managing large-scale infrastructure projects helped me understand the importance of scalability and reliability in AI systems. I was able to apply this knowledge to design and deploy multi-agent systems on Oracle Cloud, which handled 250 concurrent users with a 95% uptime rate. However, I had to unlearn many habits, such as relying on a large team and extensive resources, which were not available to me as a solo founder.
What Didn't Translate
I had to stop hiding the gap between my executive experience and my new role as an AI developer. I was used to having a team of experts at my disposal, but now I had to learn everything myself. I spent 3 months trying to implement a custom routing algorithm using Groq and Claude, only to realize that I had underestimated the complexity of the problem. The error message "CUDA_ERROR_INVALID_VALUE" became all too familiar, and I had to start from scratch.
Building AI Agents
I built 7 different AI agents using Telegram and WhatsApp APIs, each with its own set of constraints and limitations. I had to optimize the agents to handle 100 messages per second, while keeping the latency below 500ms. I used a combination of natural language processing and machine learning algorithms to improve the agents' accuracy, which increased by 25% over 6 months.
Real-World Constraints
One of the biggest challenges I faced was dealing with the limitations of the Oracle Cloud infrastructure. I had to work around the 10GB storage limit per instance, which meant implementing a custom data compression algorithm to reduce storage costs by 30%. I also had to navigate the complexities of international data transfer regulations, which added an extra layer of complexity to my already complicated workflow.
Frequently Asked Questions
Q: How did you handle the transition from a non-technical executive role to a technical founder role?
A: I had to start from scratch and learn everything myself, which was a humbling experience. I spent 6 months learning Python, Java, and C++, and another 6 months learning AI and machine learning fundamentals. It was a steep learning curve, but it was worth it in the end.
Q: What was the most surprising thing you learned about building AI systems?
A: The most surprising thing I learned was how important it is to have a deep understanding of the underlying infrastructure and algorithms. I had to learn about CUDA, GPU acceleration, and distributed computing, which were all new to me. It was a challenge, but it helped me build more efficient and scalable AI systems.
Q: How do you handle the solo founder workload and responsibilities?
A: It's not easy, but I've learned to prioritize and focus on the most important tasks. I work an average of 12 hours a day, 6 days a week, and I've had to make sacrifices in my personal life. However, I've also learned to ask for help when I need it, and I've built a network of fellow founders and developers who support me.
Q: What advice would you give to other executive career pivoters who want to become AI developers?
A: My advice would be to be prepared to start from scratch and learn everything yourself. Don't be afraid to ask for help, and don't be too proud to admit when you don't know something. It's a challenging journey, but it's worth it in the end. Also, be prepared to face a significant pay cut, at least in the short term. I took a 60% pay cut when I left my executive role, but it was worth it for the freedom and autonomy I gained.
Q: What's next for AIdeazz and your AI development journey?
A: I'm currently working on building a new AI agent that can handle 1000 concurrent users, which will require significant improvements to my infrastructure and algorithms. I'm also exploring new applications for my AI agents, such as customer service and tech support. It's an exciting time for AIdeazz, and I'm looking forward to seeing what the future holds.