Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development

In the realm of tech entrepreneurship, Paul Graham’s adage to “do things that don’t scale” serves as an important reminder for startups. Yet, the implementation of this wisdom in the realm of coding often goes unaddressed.

After dedicating eight months to developing an AI podcast platform, I’ve cultivated a unique framework that revolves around the concept of a three-month trial period for any unscalable method I implement. After this duration, each method must either showcase its value and transition into a well-built solution or be discarded.

Rethinking Scalability from a startup Perspective

As developers, we are often trained to engineer scalable solutions from the very beginning—considering design patterns, microservices, and distributed systems intended for millions of users. However, this mindset tends to reflect the ethos of larger companies rather than startups. In many cases, writing scalable code upfront becomes a costly form of procrastination, focused on potential users who might never materialize.

My three-month framework encourages me to prioritize execution over perfection. It nudges me to craft straightforward, albeit imperfect, code that achieves results and offers insights into actual user needs.

Current Infrastructure Hacks: A Lesson in Practicality

1. Single VM Setup

Running everything—database, web server, background jobs, and Redis—on a sole $40/month virtual machine, I maintain no redundancy and execute manual backups to my local system.

Why is this setup effective? I’ve gleaned critical insights about my resource requirements in just a couple of months. For example, my platform—which I anticipated to be resource-intensive—only requires 4GB of RAM at peak times. My previously planned complex Kubernetes infrastructure would have resulted in managing idle containers instead of serving users.

When the system goes down (which it has, twice), I gain valuable information about what contributes to the failures—often differing from my initial expectations.

2. Simplified Configuration Management

Instead of utilizing configuration files or environment variables, I use hardcoded constants throughout my codebase, such as:

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

This seemingly primitive approach provides a surprising benefit: I can swiftly search my code for any configuration value and track changes in