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

In the tech world, you’re likely familiar with the esteemed advice from Paul Graham: “Do things that don’t scale.” However, translating this wisdom into actionable steps can be a daunting task, particularly for developers.

Over the past eight months, as I’ve been developing an AI podcast platform, I’ve crafted a straightforward approach: every non-scalable solution is given a lifespan of three months. If it demonstrates its value within that timeframe, it gets the opportunity for refinement; otherwise, it gets discarded.

The Dilemma of Scalability in Startups

As engineers, we often have the instinct to aim for scalable solutions right from the beginning. We immerse ourselves in design patterns, microservices, and the allure of distributed systems—essentially architectures that are meant to accommodate millions of users. However, this mindset may not be the best fit for the startup environment.

In many cases, creating scalable code can lead to costly delays, targeting users who might not even be in existence. My 3-month rule encourages me to embrace straightforward, albeit less polished, coding practices that yield quick results and reveal the actual needs of my users.

My Current Infrastructure Approaches and Their Genius

1. Unified VM Management

Currently, everything for my platform is running on a single Virtual Machine (VM) costing $40 a month. This setup houses the database, web server, background jobs, and Redis. There’s zero redundancy, and I conduct manual backups to my local storage.

Why is this a smart strategy? Within two months, I’ve gained a clear understanding of my resource requirements, far more than any capacity planning document could have provided. Surprisingly, my “AI-intensive” platform only requires 4GB of RAM during peak times. The complex Kubernetes solution I nearly implemented would have resulted in managing empty containers.

Each time the system crashes (which has occurred twice), I gather valuable insights into what actually fails—typically unexpected issues.

2. Simplified Hardcoding of Configurations

Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants:

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

Adjusting any value involves redeployment, but this simplification has its benefits. I can quickly search my