Embracing Unscalable Solutions: The 3-Month Experimentation Framework in My AI Podcast Platform
In the tech industry, we often hear the insightful advice from Paul Graham: “Do things that don’t scale.” But how do we effectively embrace this principle, especially when it comes to coding and development? Over the past eight months while building my AI podcast platform, I’ve created a unique framework that could redefine how we approach our projects: the 3-Month Experimentation Rule.
The Challenge of Scalability
As engineers, our training pushes us towards crafting scalable architectures right from the onset. We delve into design patterns, microservices, and distributed systems, all of which are vital for accommodating future growth. However, in a startup context, prioritizing these scalable solutions can often lead to unnecessary complexities and wasted resources.
Instead of preparing for non-existent users or hypothetical problems, my 3-month rule encourages the development of straightforward, even “imperfect,” code. This approach helps me understand user needs in a practical, hands-on manner.
My Current Unscalable Hacks: Simplicity Is Key
1. Simplified Infrastructure on a Single VM
Rather than complicating my setup with multiple services, I run everything—from the database to the web server—on a single $40/month virtual machine. This lack of redundancy may sound like a risk, but it has taught me a lot about my actual resource usage. Within two months, I discovered that my “AI-heavy” platform only peaks at 4GB of RAM. My initial plans for a complex Kubernetes setup would have essentially been wasted on managing idle resources.
When server crashes occur (which they have), I gain immediate insights into where the failure lies, often surprising me with insights I never anticipated.
2. Hardcoded Configurations for Agility
Instead of using configuration files or environment variables, I use hardcoded constants throughout my application. Modifying any value requires a redeploy, but this setup has some hidden advantages. I can quickly search for configuration parameters across my entire codebase and track changes through version history. Over three months, I’ve made only three adjustments, saving significant engineering time while ensuring oversight.
3. Leveraging SQLite in Production
Against conventional wisdom, I’m using SQLite to support a web application with up to 50 concurrent users. My database is just 47MB and performs exceptionally well, revealing that my usage patterns consist largely of read operations. Had I chosen
