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

In the fast-paced world of tech startups, the adage “do things that don’t scale” is widely recognized, yet rare is the discussion on how to effectively implement this philosophy, particularly in the realm of coding. Over the past eight months, while developing my AI podcast platform, I devised an approach that I like to call the 3-Month Rule. This principle ensures that any unscalable coding hack I deploy is given a three-month trial period. At the end of this timeframe, it must either demonstrate its value and be refactored into a proper solution, or it will be discarded altogether.

The startup Perspective on Scalability

As engineers, we often feel compelled to architect “scalable” solutions from the very start. We get caught up in the allure of design patterns, microservices, and distributed systems—building resilient architectures that can effortlessly accommodate millions of users. However, this mindset can be detrimental in the startup environment. Expensive optimizations for hypothetical users can lead to procrastination, distracting us from addressing current, pressing needs. The 3-Month Rule empowers me to focus on simple, straightforward coding that moves the project forward while providing critical insights into user behavior.

Current Infrastructure Hacks: Smart Simplicity

1. Consolidated Operations on a Single VM

All components of my application—database, web server, background tasks—run on one $40-a-month virtual machine. While this setup lacks redundancy and relies on manual backups, it has proven invaluable. Within just two months, I gained insights into my actual resource requirements, discovering that my “AI-heavy” platform peaks at 4GB of RAM. The complex Kubernetes infrastructure I initially considered would have resulted in wasted resources managing empty containers. When the server crashes (which has happened a couple of times), I receive firsthand information on the failures that truly matter.

2. Hardcoded Configurations

My codebase features hardcoded variables for key configurations:

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

This approach, while seemingly primitive, offers significant advantages. Accessing configuration values is seamless—I can rapidly search through the entire codebase. Each adjustment is tracked in Git, and all changes are subject to review, albeit by me