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

In the realm of startup culture, one piece of advice frequently stands out: “Do things that don’t scale,” attributed to the insightful Paul Graham. While this mantra is widely embraced, the practical implementation of it, especially in coding and development, often remains elusive.

During my eight-month journey building an AI podcast platform, I found a unique way to put this philosophy into action—enter the 3-Month Rule. Essentially, any unscalable solution I employ is given a trial period of three months. At the conclusion of this period, the solution either demonstrates its worth and is refined into a robust build, or it is discarded.

Rethinking Scalability

As developers, our instinct is to create scalable solutions from the onset. We often gravitate toward established practices like utilizing design patterns and distributed systems—architectures that are designed to accommodate vast numbers of users. However, in a startup environment, this mindset can lead to unnecessary complexities and delays.

Why spend time optimizing for prospective users when the current focus should be on understanding existing needs? The 3-Month Rule encourages me to experiment with straightforward, even crude, coding strategies that lead to tangible outputs and valuable user insights.

My Ingenious Infrastructure Hacks

1. Single VM for Everything

I host my entire platform—database, web server, background processes, and more—on a single $40/month virtual machine. While this approach may seem reckless, it has delivered invaluable insights. Over just two months, I gained a clearer understanding of my resource requirements than I would have from any resource planning document. I’ve identified that my platform peaks at a mere 4GB of RAM, and the complex setup I nearly executed would have ended up managing empty containers.

When the system crashes (which has happened twice), I receive actual breakdown data, revealing the unexpected points of failure.

2. Hardcoded Configurations

Configuration constants litter my application, e.g.:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While this lack of a centralized configuration system might seem outdated, it offers exceptional agility. Locating any configuration change across the codebase takes mere seconds, and every alteration leaves a trace in git history. Updating configurations only poses a minor inconvenience, meaning