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

In the world of startups and tech innovation, the phrase “do things that don’t scale” echoes through the halls of wisdom, largely originating from the esteemed Paul Graham. However, the implementation of this advice in practical coding endeavors often remains a topic that lacks thorough exploration. Having spent the last eight months developing my AI-centric podcast platform, I’ve devised a straightforward yet powerful framework: every unscalable shortcut is given a trial period of three months. At the end of this timeframe, each hack is either validated for its value and subsequently refined, or it is retired.

As software engineers, we are typically trained to prioritize the creation of scalable solutions from the outset. We focus on sophisticated design patterns, microservices, and distributed systems, envisioning architectures that can support millions of users. While this is indeed vital for larger organizations, in the startup phase, over-engineering can sometimes amount to expensive procrastination. My 3-month rule encourages me to write simple, straightforward code that may not be perfect but is functional and enlightening, helping me to truly understand the needs of my user base.

Ingenious Infrastructure Hacks That Serve a Purpose

1. Consolidated Infrastructure on a Single VM

I’ve opted to run my database, web server, background jobs, and Redis instance on a single virtual machine costing just $40 per month, without redundancy, and performing manual backups to my local machine.

Why is this approach advantageous? In just two months, I have gained insights into my actual resource needs that no capacity planning document could provide. For instance, I discovered that my “AI-heavy” platform only requires 4GB of RAM at peak times. The complex Kubernetes architecture I nearly implemented would have meant managing empty containers, whereas when my system crashes (which has happened twice), I obtain actionable data about the real issues at hand—frequently unexpected factors.

2. Direct, Hardcoded Configurations

My configurations are hardcoded directly into the codebase:

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

This lack of external configuration files means that any modifications necessitate a redeployment. The benefit of this simplicity? I can quickly search my entire codebase for any configuration value. Changes to pricing are meticulously tracked via