Embracing the 3-Month Rule: A Practical Guide to Unscalable Solutions

In the world of startups and engineering, the advice from tech luminary Paul Graham—encouraging the pursuit of strategies that don’t scale—often goes unexamined in terms of real-world application, particularly in coding. After eight months of building an AI podcast platform, I’ve devised a framework that brings clarity to this concept: the 3-Month Rule. This guideline stipulates that any temporary, unscalable solution is given a three-month trial period to prove its merit. If it doesn’t demonstrate value within that time frame, it is discarded.

As engineers, we often fall into the trap of focusing on scalable solutions right from the start. We become captivated by design patterns, microservices, and distributed systems capable of handling vast user bases. However, this mindset is more suited for large companies and can lead to unnecessary complexities in a startup environment. Instead of making premature optimizations for potential future users, my framework prioritizes simplicity and encourages experimentation.

The Infrastructure Hacks That Illustrate My Approach

1. A Single VM Solution

All aspects of my platform, including the database, web server, and background jobs, operate on a single $40/month virtual machine. This setup may lack redundancy, and data backups are performed manually to my local machine, but here’s the beauty of it: I’ve gained invaluable insights into my resource needs quicker than any formal capacity plan could provide. My initial expectations of a “heavy” AI platform have proven wrong, peaking at merely 4GB of RAM. The overly complex Kubernetes structure I almost implemented would have managed non-existent resources.

2. Directly Hardcoded Configurations

Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase. Values like pricing tiers and user limits are scattered in the code, which means that modifying any of them requires redeployment. While this might seem inefficient, the advantages are clear: I can search for any configuration quickly, and every change is traceable in the project’s Git history. In total, I have modified these values only three times over the past three months—a stark contrast to the time I would have spent developing a configuration service that might take a week to implement.

3. Using SQLite in Production

Despite being a multi-user web application, my entire database remains a mere 47MB with SQLite handling it effortlessly. With access patterns revealing