Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions

In the world of startups, the often-quoted advice from Paul Graham to “do things that don’t scale” is easier said than done, especially in the realm of coding. As I embarked on the journey of developing my AI podcast platform over the past eight months, I realized the necessity of a structured approach to implementing this philosophy. Thus, I established the “3-Month Rule”: any unscalable solution gets a trial period of three months. After this period, it either demonstrates its worth and evolves into a robust solution or it is retired.

As engineers, we’re inclined to pursue scalable solutions from the outset, often fixating on architectural elegance like microservices and distributed systems that can support millions of users. However, in a startup environment, this mindset can lead to costly procrastination, as we optimize for users that are yet to come and tackle issues that might never arise. The 3-Month Rule compels me to focus on creating straightforward, functional code that can be deployed rapidly, allowing me to better understand the genuine needs of users.

My Current Infrastructure Hacks: A Smarter Approach

1. Consolidated Infrastructure on a Single VM

Currently, my database, web server, background jobs, and caching all operate on one modest $40/month virtual machine, with zero redundancies and manual backups.

While this setup may seem rudimentary, it has proven invaluable. Over the last two months, I have gleaned more insights about my actual resource utilization than any prediction document could provide. My “AI-heavy” platform has shown that it only requires 4GB of RAM, revealing that my anticipated complex Kubernetes setup was unnecessary, potentially just adding overhead without real benefit. Each time my system encounters a crash (twice to date), I receive precise feedback about the actual failure points, which are rarely what I expect.

2. Direct Configuration Without Complexity

By employing hardcoded configurations throughout my codebase—like setting prices and defining maximum user capacities—I eliminate the need for complex configuration management.

This approach, while seemingly limiting, has its advantages. I can rapidly find any configuration value with a simple search command, and any modifications are documented in my version control history. This practice allows for quick deployment adjustments, taking minutes rather than the extensive time that a detailed configuration service would require.

3. Utilizing SQLite for Efficient Performance

Rather than opting for more