The 3-Month Strategy: A Pragmatic Approach to Non-Scalable Development

In the realm of entrepreneurship, one piece of advice resonates universally: “Do things that don’t scale.” Yet, when it comes to applying this principle in the world of coding, practical guidance is often hard to come by. Over the past eight months while developing my AI podcast platform, I’ve crafted a straightforward framework to navigate this challenge. My solution? Implement a 3-month lifespan for any unscalable hack. After this period, each hack must either prove its worth and be refined into a scalable solution or be phased out entirely.

A Shift in Mindset

As engineers, we’re typically conditioned to prioritize scalable infrastructure right from the outset. We dream of intricate design patterns, microservices, and robust distributed systems capable of supporting millions of users. However, this mindset can lead to unnecessary complexities, particularly in a startup environment where the demand for scalability often remains elusive.

In reality, focusing on scalable solutions too early can hinder progress, as it optimizes for theoretical users and addresses problems that might never surface. My 3-month rule encourages me to produce straightforward, albeit imperfect, code that can be quickly deployed, enabling me to gather invaluable insights into user needs.

Current Infrastructure Hacks: Simplicity with Purpose

Here are some of my current development choices, and why they’re not just compromises, but strategic decisions:

1. All-in-One Server Approach

I’ve consolidated my database, web server, and background jobs onto a single $40/month virtual machine. While it lacks redundancy and relies on manual backups, this choice has provided clarity about my actual resource consumption. In just two months, I discovered my platform typically requires only 4GB of RAM. Instead of wasting resources on a complex Kubernetes setup, I’ve gained critical data on what truly causes system failures, which often surprises me.

2. Hardcoded Configurations

My configuration is hardcoded directly into the application – no configuration files or environment variables. For example:

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

This method allows me to quickly search for any configuration value across the codebase. Each update requires a simple redeployment, saving countless hours of engineering time. Over the past three months, I’ve only needed to adjust these values three times, reflecting