Embracing the Three-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development

In the world of startups, the mantra often heard is Paul Graham’s wise counsel: “Do things that don’t scale.” However, the challenge lies in how to effectively translate that advice into actionable code. After eight months of developing my AI podcast platform, I’ve crafted a straightforward methodology: every unscalable solution gets a dedicated lifespan of three months. Following this period, it either demonstrates its worth and evolves into a robust solution, or it’s phased out.

As software engineers, we often fall into the trap of crafting scalable architectures from the get-go—think distributed systems, microservices, and design patterns tailored for thousands or millions of users. While this big-company thinking serves a purpose, it can be a hindrance in the startup environment. Here, striving for scalability too soon usually results in costly delays, as we end up catering to hypothetical users and creating solutions for problems that may never arise.

My three-month rule compels me to produce simpler, albeit imperfect code. This approach enables me to rapidly deliver features while gaining firsthand insights into user requirements.

Innovative Infrastructure Hacks That Deliver Real Insights

1. Consolidated Operations on a Single Virtual Machine

I’ve opted to run my database, web server, background processes, and even Redis all on a single $40 monthly virtual machine—without any backup redundancy, relying solely on manual backups to my local storage.

While some might deem this approach reckless, the reality is that I’ve gleaned more about my actual usage patterns in just two months than any lengthy capacity forecast could provide. For instance, I discovered my AI-driven platform only requires about 4GB of RAM during peak times. Creating an intricate Kubernetes setup would have meant managing mostly idle containers.

Interestingly, when my system encounters crashes (which have happened twice), I gain valuable insights into the factors causing the issues—something that often surprises me.

2. Simplified Configuration Management

In my code, you won’t find complex configuration files or endless environment variables. Instead, constants are embedded within the code to manage pricing and user limits.

This seemingly simplistic approach actually offers substantial advantages: I can quickly search through my entire codebase for any configuration value and track changes seamlessly through git history. Since I’ve altered these values only three times in the last three months, the time saved through this method is significant, totaling around 15 minutes for redeployment versus countless hours