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

In the world of startups and technology, one piece of advice often echoes through the halls of innovation: “Do things that don’t scale,” as championed by Paul Graham. While this concept is frequently mentioned, there remains a significant gap in understanding how to practically apply it, especially in coding and development.

After spending eight months creating an AI podcast platform, I’ve crafted a straightforward framework that I call the “3-Month Rule.” This approach gives each non-scalable solution a specific lifespan of three months. At the end of this period, we evaluate its effectiveness—if it adds value, it gets a robust build; if not, it gets the axe.

As engineers, our training often nudges us towards scalable solutions right from the outset. We’re prepared to deploy intricate designs, microservices, and distributed systems—architecture aimed at accommodating millions of users. However, for startups, such scale can be an exercise in futility, leading to what I term “expensive procrastination.” My 3-month strategy encourages the development of simple, functional code that gets deployed quickly, allowing us to truly understand our users’ needs.

Current Implementations: Simplistic Yet Strategic

1. Consolidation on a Single Virtual Machine

Hosting all components—database, web server, background tasks, and caching—on a single $40 monthly VM might seem rudimentary. Yet, this setup has provided invaluable insights into my actual resource requirements. Within just two months, I’ve learned my platform peaks at 4GB of RAM, which means the sophisticated Kubernetes architecture I nearly implemented would have simply managed dormant containers. During crashes, I gain clear data on which elements fail, surprising me with the insights revealed.

2. Hardcoded Configurations Throughout

Rather than relying on configuration files or environment variables, I’ve opted for direct constants within the code:

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

This method may appear primitive, but it’s a hidden gem. I can quickly search for any configuration value across the entire codebase and track changes in the git history. Over the past three months, I’ve only modified these constants three times—meaning a rapid redeploy of 15 minutes versus the potential 40 hours