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

In the world of startups, where resources are often limited and agility is paramount, the adage “Do things that don’t scale” resonates deeply, especially among tech entrepreneurs. However, the challenge lies in translating this advice into practical coding strategies. Over the past eight months while developing my AI podcast platform, I’ve devised a framework that tackles this head-on: the 3-Month Rule. This principle essentially states that any unscalable hack I implement should be evaluated within three months—either it proves its effectiveness and is refined, or it gets discarded.

Rethinking Scalability in Startups

As engineers, our training often pushes us toward creating scalable solutions from the outset. We become enamored with sophisticated design patterns, microservices architectures, and systems that can cater to millions of users. Yet, this mindset can be a trap for startups, leading to expensive delays and resource allocation for challenges that may never arise. Embracing the 3-Month Rule compels me to prioritize simplicity and directness in coding so that I can better understand the true needs of my users.

My Ingenious Infrastructure Hacks

Here’s a closer look at the unconventional yet insightful methods I’ve employed during this journey:

1. Consolidated Resources on a Single VM

I host my database, web server, background jobs, and Redis all on a single virtual machine costing just $40 a month, accepting the trade-off of zero redundancy and manually backed-up data. This seemingly risky choice has revealed more about my actual resource needs in two months than any traditional capacity planning could. For instance, I discovered that my “AI-heavy” platform only peaks at 4GB of RAM, highlighting that the Kubernetes setup I contemplated would have been overkill, forcing me to manage empty containers instead.

2. Simple Hardcoded Configurations

My configuration values are hardcoded into the application:

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

Rather than using complex configuration files or environmental variables, I opt for hard-coded constants. While this might seem limiting, it allows me to quickly search for and manage configuration values seamlessly. With only three updates in three months, the time spent on redeployment (15 minutes) is a far cry from