Embracing the 3-Month Rule: A Pragmatic Approach to Building Scalable Software

In the world of startups, the familiar mantra from tech-savvy entrepreneur Paul Graham—“Do things that don’t scale”—is often echoed but rarely dissected. The challenge lies in how to apply this principle effectively in software development. After spending eight months developing an AI podcast platform, I’ve crafted a straightforward strategy that I call the “3-Month Rule.” This approach allows unscalable solutions to thrive temporarily, encouraging rapid learning and adaptation.

The Premise of the 3-Month Rule

As software engineers, we are trained to architect scalable solutions right from the get-go—think complex design patterns, microservices, and distributed architectures that can accommodate millions of users. However, in the startup realm, focusing on scalability too early can lead to wasted resources, as it often involves addressing hypothetical user bases and challenges that may never materialize.

By enforcing my 3-month timeframe, I can focus on writing simple, less-than-perfect code that gets shipped quickly, allowing me to understand user needs far better than any theoretical framework could provide.

Innovative Infrastructure Solutions

Here are the core strategies I’ve implemented in my current infrastructure—each designed to encourage learning while prioritizing immediacy over perfection:

1. Simplified Single VM Architecture

I operate my entire application—database, web server, background tasks, and caching—on one modest $40/month virtual machine without redundancy or automated backups. While this setup may seem reckless, it has provided critical insights into my resource requirements. Within two months, I’ve learned that my AI-driven platform operates comfortably on just 4GB of RAM. The complex Kubernetes environment I initially considered would have only complicated management without offering any real benefit.

2. Directly Hardcoded Configuration

Instead of relying on external configuration files or environment variables, I’ve opted for hardcoded constants sprinkled throughout my codebase. This approach may seem archaic, but it allows me to quickly locate and modify configuration settings, with any changes tracked in version control. In three months, I’ve made a mere three adjustments, saving me significant engineering time over the lengthy process of developing a dedicated configuration service.

3. Leveraging SQLite in Production

I chose SQLite for my multi-user web application, which has proven effective given my app’s 47MB database size and high read-to-write ratio. This choice has revealed that the vast majority of user interactions are