Embracing the 3-Month Rule: A Pragmatic Approach to Building What Works

In the tech world, one adage resonates widely: “Do things that don’t scale.” While this wisdom can be transformative for startups, particularly in coding practices, the implementation strategies often lack clarity. As I have navigated the journey of developing my AI podcast platform over the past eight months, I’ve adopted a straightforward yet effective framework that I like to call the 3-Month Rule.

The Essence of the 3-Month Rule

The premise of my strategy is simple: any hack or workaround that isn’t designed for scalability gets precisely three months to demonstrate its worth. If it proves beneficial within that time, it gets reshaped into a robust solution; otherwise, it’s discarded.

As engineers, we are often conditioned to pursue scalable solutions right from the start, delving into sophisticated architectures like microservices or distributed systems aimed at supporting millions of users. However, this kind of thinking is typically more applicable in larger organizations. For startups, overly focusing on scalable solutions can lead to unnecessary complexity, as we may be preparing for users that don’t even exist yet. Implementing the 3-Month Rule allows me to prioritize simplicity and effectiveness, creating “imperfect” yet functional code that provides real insights into user needs.

Current Infrastructure Strategies: Intellingent Choices

1. Centralized Operations on One Virtual Machine

All aspects of my platform—including the database, web server, background jobs, and caching—run on a single $40/month virtual machine. This approach may seem reckless, but it’s been invaluable. I’ve gleaned more about my platform’s resource demands in the last two months than any theoretical planning can provide. The revelation that my project peaks at just 4GB of RAM illustrates that my earlier inclination toward complex setups was misplaced.

When issues occur (and they have), I receive precise feedback regarding what truly fails. Surprisingly, it’s seldom what I anticipate.

2. Directly Hardcoded Configurations

Instead of employing configuration files or environment variables, I use hardcoded values across my codebase:

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

This choice might seem archaic, but it grants me the ability to quickly search configurations throughout my entire project. Any changes I