The 3-Month Rule: A Pragmatic Approach to Learning Through Unscalable Solutions

In the world of startups, the advice to “do things that don’t scale,” as emphasized by Paul Graham, is widely recognized but often underexplored when it comes to practical application, particularly in the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule,” where every temporary hack is granted a lifespan of three months. At the end of this period, the hack either validates its worth and is transformed into a robust solution or it’s obsolete.

As engineers, our training typically leans toward crafting scalable solutions from the outset, incorporating design patterns, microservices, and distributed systems that are capable of catering to millions of users. Yet, this type of thinking can be a pitfall for startups. Focusing on scalable infrastructure too early often leads to expensive delays in addressing real user needs. With my 3-Month Rule, I embrace the necessity of writing simplified code that can be deployed quickly, ensuring I learn what my users genuinely require.

Ingenious Infrastructure Hacks That Distill Experience

1. Consolidated Operations on a Single VM

Currently, everything from the database to the web server and background tasks runs on a single $40/month virtual machine. While this setup may seem precarious due to its lack of redundancy and reliance on manual backups, it has provided invaluable insights into my resource requirements. Within just two months, I discovered that my “AI-heavy” platform only peaks at 4GB of RAM—information that would have eluded me had I opted for a complex Kubernetes setup to manage empty containers. Crashes have been revealing, offering unexpected data on system vulnerabilities.

2. Hardcoded Configurations Throughout

Operating with hardcoded values like:

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

means no configuration files or environment variables; simply constants embedded throughout the code. While some may view this as a drawback, it allows for rapid changes and easy tracking through version control history. In the last three months, I’ve found that modifying these values has required minimal redeployment time, sidestepping the need for a cumbersome configuration service that could take a week to set up.

3. Utilizing SQLite in a Multi-User Environment