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August 28, 2025
Ten months ago, I made a $100,000 bet on building my product. Today, that product generates over $150,000 in annual recurring revenue. But here's the thing that keeps me up at night: if I were starting today, I'd do everything completely differently.
The technology landscape has evolved so rapidly that the traditional development approach I used feels almost primitive compared to what's possible now. I spent six figures working with an outsourced engineering team using older methods, when today's AI-powered tools could have gotten me to market faster and cheaper.
But that expensive education taught me something invaluable: a systematic development process that works regardless of the tools you use. Here's the exact workflow that took my product from concept to six-figure ARR, and how you can adapt it with today's advanced technology.
When I started Theanna in November last year, the AI coding tools we have today simply weren't good enough yet. I was stuck in the traditional development cycle: hire an engineering team, write detailed specs, wait for iterations, test, and repeat. It worked, but it was expensive and slow.
That $100,000 investment came from two sources: a loan from my parents (which I pay back monthly like a credit card) and revenue from a successful accelerator I had run previously. The money got me where I needed to be, but looking back, I could have achieved the same results for a fraction of the cost with today's tools.
The lesson isn't that I made a mistake - it's that technology moves fast, and the development approach that worked 10 months ago might be outdated today. You have to adapt your process as better tools become available.
The breakthrough came when I stopped thinking about building "a product" and started thinking about building systems of components. Instead of tackling one massive project, I broke everything down into discrete, manageable pieces.
Here's how the system works: Theanna has three major components, each broken down into five or six sub-components. We focus on one main component first, then systematically work through its sub-components in order of priority and architectural importance before moving to the next major piece.
This approach solved multiple problems at once. My engineer could understand how each piece fit into the larger vision. I could get customer feedback on individual components before they were integrated. And we could deploy and test pieces incrementally instead of waiting for a massive launch.
Every component starts the same way: on paper. I use graph paper to sketch out different modules and how I want them to look and feel, thinking not just about today's architecture but how each piece will evolve over time.
Once I have the core concept on paper, I move to Figma to design the three or four main pages that represent the heart of that component. This isn't about making things pretty - it's about working through the logic and user flow before any code gets written.
The key insight: design and engineering tools won't give you the result you need if you jump straight into them. You need to think through the problems on paper first, then use tools to execute your vision. This upfront design work prevents expensive engineering changes later.
After parting ways with my original engineering team in June, I partnered with an in-house engineer who's been a friend and collaborator for 12 years. Now we use a completely different development stack that takes advantage of modern AI tools.
My workflow now looks like this: Design in Figma, prototype in Claude Code, then hand detailed specifications to my engineer. I use Claude Code specifically because our backend is in Angular, not React like most no-code tools. Claude Opus excels at creating interfaces, so I develop everything by components in Claude Code first.
The game-changer is that I'm not using these tools to deploy to production - I'm using them for rapid prototyping and specification creation. This lets me get feedback from customers on working prototypes before we commit engineering resources to building the real thing.
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Organization becomes critical when you're building component by component. I manage everything in Linear, which is like a simpler version of JIRA designed for smaller teams and startups.
Every component becomes a series of tickets marked by project, milestone, and specific requirements. But here's the secret: I don't write these requirements from scratch. After I finish prototyping the frontend in Claude, I ask it to generate a complete Product Requirements Document.
This AI-generated PRD covers frontend specifications, backend requirements, edge cases, best practices, and everything else an engineer needs to build to standard. I review it and trim unnecessary complexity, but it gives us a professional foundation to work from.
The development cycle follows a strict pattern: my engineer works on specific tickets for a few days, then deploys to a User Acceptance Testing environment. He assigns the ticket back to me, and I test everything thoroughly.
If it works perfectly, I mark it complete. If there are issues, I don't just say "this doesn't work" - I create a Loom video showing exactly what I'm experiencing, write detailed notes about expected versus actual behavior, and try to identify what might be causing each bug.
This feedback loop continues until we're ready to push to production. Then comes regression testing, because things always break when you deploy to production. Only after everything is thoroughly tested do we announce the new feature to customers.
If I were starting this journey today with access to current AI tools, I'd follow the same systematic process but with dramatically different execution. The component-based approach, design-first workflow, and systematic testing would remain the same.
The difference would be speed and cost. Modern AI coding tools could handle much more of the initial development work, reducing the time from design to working prototype from weeks to days. The systematic approach I developed over 10 months of expensive learning could be compressed into a much shorter, cheaper timeline.
The most expensive mistake you can make is not learning from other people's expensive mistakes. I spent $100,000 learning how to build products systematically, but you don't have to.
The systematic approach I've outlined works regardless of whether you're using traditional development teams or modern AI tools. The key is having a process that breaks complex products into manageable pieces, gets feedback early, and maintains quality throughout.
Start with one component. Design it on paper, prototype it with AI tools, get feedback from potential customers, then build it properly. The technology has evolved to make this process faster and cheaper than ever, but the systematic approach remains the same.
Your product doesn't have to cost $100,000 to generate $150,000 in revenue. But it does need a system that ensures you're building the right thing in the right order. Copy this process, adapt it to modern tools, and build something that works.
Design and Prototyping:
Project Management:
Testing and Communication:
Alternative AI Coding Tools:
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