Embracing the 3-Month Rule: A Practical Approach to Unscalable Coding

In the world of tech startups, the age-old advice from Paul Graham resonates deeply: “Do things that don’t scale.” Yet, the practical application of this concept in programming often gets overlooked. With just eight months invested in building my AI podcast platform, I’ve developed a straightforward framework known as the “3-Month Rule.” Essentially, any non-scalable hack I implement has a set lifespan of three months, after which it must either prove its worth and be properly constructed or be discarded.

The startup Mindset vs. Engineering Best Practices

As software engineers, we often feel conditioned to create scalable solutions right from the outset. We indulge in complex architectures, such as microservices and distributed systems, all designed to support a vast number of users. However, in a startup environment, pursuing scalability too early can sometimes hinder progress. More often than not, these well-designed systems become costly delays, optimizing for hypothetical users and anticipated challenges. By adhering to my 3-Month Rule, I’ve learned to sacrifice elegance for simplicity, allowing me to deliver functional code that reveals genuine user needs.

Current Infrastructure Approaches: Smart Hacks for Real Learning

Here are some of the unconventional strategies I’ve employed that, while seemingly less sophisticated, have proven to be outsmarted.

1. Unified VM Hosting

All aspects of my platform—database, web server, Redis, and background jobs—operate on a single, cost-effective $40 per month virtual machine. There is no redundancy; I perform manual backups to my local system.

The insight here? Within just two months, I’ve gained a better understanding of my resource requirements than any capacity planning document could provide. My heavily AI-focused platform reached a peak of only 4GB RAM. The intricate Kubernetes setup I considered would have been managing idle containers. Each time I encounter a crash (which has happened twice), it provides valuable data about what truly fails—information that often surprises me.

2. Embracing Hardcoded Configurations

Instead of relying on configuration files or environment variables, I’ve chosen to hardcode key values directly into my application. This means changes require redeployment, but it also streamlines the process.

The brilliance of this approach lies in its simplicity. I can swiftly grep through my codebase for any configuration value. Every price change is tracked in Git history, and updates undergo thorough code review—even if it’s just my eye