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

In the realm of startups, where speed and learning often trump perfection, the well-known advice from Paul Graham — “Do things that don’t scale” — takes on significant relevance. However, the question remains: how do we apply this concept in a technical context, particularly in coding? After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward framework to navigate this process, which I call the Three-Month Rule.

The Three-Month Rule Explained

The premise of my approach is simple: any unscalable hack I implement is allotted a three-month lifespan. During this time, it must either demonstrate its worth and warrant a proper build, or it will be phased out. As engineers, we are instinctively trained to pursue scalable solutions from the outset. We get caught up in design patterns, microservices, and complex architectures designed to manage millions of users. However, this mindset often leads to wasted time and resources, particularly in the startup environment where the user base is yet to be established.

In my experience, focusing on scalable code can become a form of costly procrastination. The Three-Month Rule drives me to create straightforward, sometimes deemed “inefficient” code that can be quickly deployed. This approach allows me to better understand what my users truly need.

Current Infrastructure Strategies That Prove Effective

Here are some of the key strategies I’ve adopted that exemplify this philosophy, and how they foster innovation rather than hinder it:

1. One Virtual Machine for Everything

All aspects of my platform — from the database to the web server and background jobs — operate on a single $40/month virtual machine. While some may argue this lacks redundancy and safeguards, I see it as a calculated risk. This configuration has provided me with invaluable insights into my actual resource requirements far more effectively than any intricate planning document could. I quickly learned that my system peaks at just 4GB of RAM, and the elaborate Kubernetes setup I considered would have simply managed empty containers.

When outages have occurred, I’ve gained direct knowledge about the failure points, which often surprised me.

2. Hardcoded Configurations

In my code, configurations like pricing tiers and user limits are hardcoded, eliminating the complexities of configuration files or environment variables. While this might seem counterproductive, the benefit is remarkable: I can swiftly search my codebase for any value, and each