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

In the world of technology startup development, one mantra stands out among the rest: “Do things that don’t scale.” While Paul Graham’s wisdom rings true, the challenge lies in knowing how to effectively apply this principle, especially when it comes to coding and engineering.

Having spent the past eight months constructing my AI podcast platform, I’ve devised a straightforward framework that I like to call the 3-Month Rule. This approach stipulates that every unscalable solution I implement is given a lifespan of only three months. After that period, each solution will either be validated as effective enough to warrant a permanent build-out or it will be discarded.

The startup Dilemma: Overengineering vs. Practicality

As engineers, we are often trained to create scalable infrastructure right from the outset. Concepts like design patterns, microservices, and distributed systems are part of our DNA, all aimed at accommodating vast numbers of users. Yet, in the startup environment, this perspective may lead to unnecessary complexities and delays—what I refer to as “expensive procrastination.”

The 3-Month Rule compels me to prioritize quick execution over perfection, allowing me to focus on writing straightforward, albeit imperfect, code that is pressed into service quickly and reveals users’ actual needs.

Current Infrastructure: Smart Hacks that Actually Work

Below are some of my current hacks, which may seem rudimentary at first glance but have proven to be incredibly insightful and effective:

1. Consolidated Resources on a Single Virtual Machine

I have all essential services—database, web server, background jobs, and caching—running on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups.

The advantage? In just two months, I have gained invaluable insights into my resource necessities that any capacity planning document would have failed to elucidate. It turns out my “AI-centric” platform only requires around 4GB of RAM during peak usage, informing me that the complicated Kubernetes setup I nearly pursued would have led to wasted resources.

2. Hardcoded Configurations Without External Dependencies

Instead of using configuration files or environment variables, I have opted for hardcoded constants throughout my code:

“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL