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

In the tech industry, we frequently hear the mantra from Paul Graham: “Do things that don’t scale.” Yet, when it comes to practical application—especially in coding—few address how to effectively implement this principle. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” It stipulates that any unscalable technique only remains in use for three months. At the end of this period, if the hack has not demonstrated its value, it will be retired.

Rethinking Scalability

As engineers, we are often conditioned to seek scalable solutions from the inception of our projects. We immerse ourselves in design patterns, microservices, and distributed systems—concepts that are essential for servicing millions of users. However, this mindset can be counterproductive in a startup environment where scalability may equate to costly delays. Too often, we find ourselves optimizing for users that don’t yet exist, solving potential problems that may never arise.

The 3-Month Rule encourages me to produce straightforward, practical, albeit “imperfect” code that gets delivered and provides real insights into user needs.

My Current Infrastructure Hacks: Embracing the Unconventional

Below are some of the unconventional strategies I’ve employed that have proven to be surprisingly effective:

1. Consolidating Services on a Single Virtual Machine

I run my database, web server, background jobs, and Redis all on one budget-friendly $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local drive. Why is this a wise decision? Over the past two months, I’ve gained valuable insights into the true resource requirements of my platform. It turns out that my initially perceived “heavy” requirements only peak at 4GB of RAM, and the complex Kubernetes environment I nearly constructed would merely handle idle containers.

Moreover, when the system crashes (which has happened twice), I get to pinpoint exactly what fails—a refreshing contrast to my original assumptions.

2. Utilizing Hardcoded Configuration

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

Instead of using separate configuration files or environment variables, I’ve opted for hardcoded constants spread across my files. Changing a setting