Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Coding

In the world of startups and innovation, the adage “Do things that don’t scale,” famously imparted by Paul Graham, often comes up as a guiding principle. However, the practical application of this advice—especially within the realm of coding—tends to be overlooked. Over the past eight months, as I’ve been building my AI podcast platform, I’ve refined a straightforward approach: I call it the 3-Month Rule. This framework stipulates that any unscalable hack in my codebase is granted a trial period of three months. At the end of this timeframe, the hack must either prove its worth and evolve into a more robust solution, or it will be discarded.

The startup Mindset: Why Quick and Dirty Wins

As software engineers, we are typically trained to prioritize scalable solutions right from the start. We aim for the perfection of design patterns, microservices, and distributed systems, all of which are essential for handling a high volume of users. However, this mindset can be detrimental in a startup environment, where focusing on scalability without actual user data often leads to unnecessary complexity and expense.

In a startup, attempting to build scalable code from the outset can frequently result in delayed progress. In practice, it often becomes a form of costly procrastination. By adhering to the 3-Month Rule, I am encouraged to produce straightforward, albeit imperfect, code that can be deployed quickly. This approach not only helps me deliver features but also provides invaluable insights into user behavior and needs.

Strategic Infrastructure Simplifications

Here are some of my current infrastructural choices that may seem unconventional at first glance, but are actually strategic in their simplicity:

1. All-in-One Virtual Machine Setup

I run my entire application—database, web server, background jobs, and Redis—on a single $40 per month virtual machine. This means no redundancy and manual backups to my local machine.

Why is this a savvy move? This setup has allowed me to gain deeper insights into my actual resource requirements in just two months, far beyond what any capacity planning document could provide. The reality is that my platform’s peak resource usage stands at 4GB of RAM, suggesting that the Kubernetes infrastructure I almost implemented would have been wasted managing non-existent workloads.

2. Hardcoded Configuration

Parameters like pricing and user limits are hardcoded directly in my