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

In the world of startups, the mantra “Do things that don’t scale” is often espoused, but few delves into the practical implementation of this advice, particularly in the context of coding. Drawing from my experiences over the past eight months while building an AI podcast platform, I’ve developed a framework that I call the 3-Month Rule. This principle allows me to evaluate unscalable hacks: they get a trial period of three months to prove their worth. If they don’t, they’re phased out.

The typical mindset of engineers is to create solutions that are scalable from the outset. Concepts such as microservices and distributed systems are designed to accommodate vast numbers of users. However, in the startup environment, investing time and resources into scalable architecture too soon can be a misstep; it often leads to unnecessary complexity for problems that may never surface. My 3-Month Rule compels me to develop straightforward and “imperfect” code that actually delivers value and reveals true user demands.

Unpacking My Pragmatic Infrastructure Choices

Here’s a glimpse into my current infrastructure hacks, which at first glance may seem simplistic or even misguided, yet they have proven to be effective in their simplicity:

1. Consolidated Operations on a Single Virtual Machine

I run everything—database, web server, background jobs, and caching—on a single virtual machine that costs just $40 monthly. This approach has no redundancy and relies on manual backups. While it seems risky, it has offered me insights into my resource usage that extensive capacity planning documentation would never provide. For instance, my AI platform typically peaks at 4GB of RAM, revealing that a more complex Kubernetes setup would have been overkill.

2. Static Configuration Across the Codebase

I have hardcoded essential settings like pricing and user limits directly in the code. This might not follow conventional wisdom, but it allows me to track changes easily through version control and minimizes the time spent on engineering tasks. I’ve altered these values three times in three months—a task that took a mere 15 minutes instead of the extensive hours required to build and maintain a separate configuration service.

3. Leveraging SQLite for a Multi-User Environment

Yes, I’m currently using SQLite in production for my web application. This compact 47MB database manages 50 concurrent users seamlessly. My usage patterns revealed a 95% read-to