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

In the realm of technology startups, one piece of advice is often echoed: “Do things that don’t scale.” This insight, commonly attributed to Paul Graham, is often discussed in theory, but rarely explored in practical terms, especially in the field of coding. As I embark on my journey to develop an AI podcast platform, I have devised a straightforward framework that I call the “3-Month Rule.” This method allows me to experiment with unscalable solutions for a defined period of three months. At the end of this timeframe, each hack must either demonstrate its value and evolve into a robust solution or be discarded altogether.

The Dilemma of startup Engineering

As technical professionals, we are conditioned to design scalable solutions right from the outset. Our training immerses us in concepts such as microservices, distributed systems, and sophisticated design patterns—architectural marvels that can accommodate millions of users. Yet, for startups, pursuing scalability too early can be a trap, often leading to wasted resources while delaying meaningful progress. My 3-month rule compels me to develop straightforward, even “imperfect” code that can be deployed rapidly, allowing me to gain insights into genuine user needs.

My Current Hacks: Pragmatic Choices for Learning

1. Consolidation onto a Single VM

By running my database, web server, and background processes all on one $40/month virtual machine, I am embracing simplicity over redundancy. While this setup lacks backup systems, it has afforded me invaluable insights into my resource utilization in just two months. I discovered that my platform, which I initially anticipated would be resource-intensive, only required a modest 4GB of RAM. Had I opted for a complex Kubernetes architecture, I would have been left managing an array of empty containers.

2. Hardcoded Configuration Throughout

My codebase is filled with constants like:

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

This approach—bypassing the use of configuration files and environment variables—may sound counterintuitive. However, the ability to search my code quickly for configuration values has proven advantageous, allowing me to track changes precisely through Git history. Modifying any settings requires a simple redeployment, saving what could have been endless hours