Embracing the 3-Month Rule: A Practical Framework for Non-Scalable Solutions

In the startup world, the age-old wisdom from Paul Graham resonates strongly: “Do things that don’t scale.” However, implementing this philosophy in the realm of coding often goes unaddressed. After eight months of developing my AI podcast platform, I’ve discovered a straightforward approach: every unscalable workaround receives a trial period of three months. Post-trial, it either demonstrates its value and is transformed into a sustainable solution, or it is discarded.

As engineers, we are conditioned to craft scalable solutions right from the start—design patterns, microservices, and sophisticated systems meant to accommodate millions of users. While this is indeed admirable, it’s a mindset that often hinders smaller teams.

In a startup, prioritizing scalability can become a costly form of procrastination, where we optimize for hypothetical users and problems that may never materialize. My 3-month rule compels me to deploy straightforward, sometimes inefficient code that focuses on real user needs.

Current Infrastructure Tactics: Smart Unscalability

1. Single Virtual Machine Environment

I’ve opted for a single virtual machine that hosts my entire infrastructure—database, web server, background jobs, and caching—at a modest cost of $40 per month. While this setup is devoid of redundancy and relies on manual backups, it has provided invaluable insights into my actual resource requirements. Within two months, I discovered that the peak demand for my “AI-heavy” platform only reached 4GB of RAM. The elaborate Kubernetes architecture I once considered would have merely managed unused containers—an unnecessary complexity.

When the system crashed, which it did twice, it revealed exactly what broke—information that I would have never anticipated.

2. Hardcoded Configurations

Rather than using configuration files or environment variables, my application relies on hardcoded constants spread throughout the codebase. For instance:

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

This approach may seem impractical; however, it has a hidden advantage. I can swiftly search for configuration values across my codebase and ensure that every change stays recorded in the git history. In the span of three months, I’ve redeployed to accommodate just three changes—spending a mere 15 minutes rather than the estimated 40