Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Software Development

In the entrepreneurial tech world, one piece of wisdom often stands out: Paul Graham’s directive to “do things that don’t scale.” While this advice is commonly shared, the question of how to effectively apply it, especially in coding and software development, often goes unaddressed. After eight months of developing my AI podcast platform, I’ve discovered a practical methodology that might benefit others: the 3-Month Rule. This simple principle states that each unscalable solution is given a trial period of three months. After that timeframe, we either assess its value and formalize it into a scalable system, or we let it go.

The Challenge of Building for Scalability

As software engineers, we often instinctively design for scalability, focusing on advanced architectures like microservices, distributed systems, and elegant design patterns right from the start. However, in a startup environment, this approach can lead to costly procrastination—spending time and resources optimizing for theoretical future users and solving problems that may never arise. The 3-Month Rule encourages the creation of straightforward, albeit less refined, code that is deployable and helps reveal our users’ actual needs.

My Current Infrastructure Hacks: Strategic and Insightful

1. All Operations on a Single Virtual Machine

I run everything—database, web server, background jobs, and caching—on one $40-per-month VM with zero redundancy. Although this may seem reckless, it has yielded invaluable insights about my actual resource utilization in just two months. I learned that my “AI-heavy” platform peaks at 4GB of RAM, which would have been wasted on a complex Kubernetes setup that I initially considered. When the system has crashed (which has happened twice), I’ve gained real-world data on failure points—surprisingly, they’re often different from my initial assumptions.

2. Hardcoded Configurations

Without config files or environment variables, my configurations are hardcoded constants. Any changes necessitate a redeployment. While this may seem inefficient, it allows me to quickly locate and track configuration values across the codebase. Over the past three months, I’ve only adjusted these values three times, saving significant engineering effort by choosing a simpler path.

3. Utilizing SQLite in Production

Despite running a multi-user web application, my database remains a compact 47MB SQLite instance. It effectively manages up to 50 concurrent users with