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

In the tech world, many entrepreneurs and developers are familiar with the wisdom imparted by Paul Graham: “Do things that don’t scale.” However, the execution of this advice, particularly in software development, is often overlooked. After eight months of building my AI podcast platform, I’ve crafted a straightforward framework: every non-scalable approach is given a lifespan of just three months. By the end of this period, it must either demonstrate its value and be developed further or be phased out.

As engineers, we often gravitate toward scalable solutions from the outset—embracing design patterns, microservices, and complex architectures capable of accommodating millions of users. While this is crucial in large corporations, such thinking can introduce unnecessary complexities at a startup level. In these early stages, prioritizing scalable code can often mean procrastinating; we risk devoting time to optimizing systems for imagined users while neglecting the actual needs of our existing customer base.

Letting Go of Perfection: My Infrastructure Hacks

Here’s a glimpse into my current infrastructure decisions and why I believe they are strategically advantageous:

1. Simplified Setup on a Single VM

I’ve consolidated my database, web server, background jobs, and caching on one $40/month virtual machine. While this approach lacks redundancy and relies on manual backups, the insights gained in two months have far surpassed any extensive capacity planning. Surprisingly, my resource usage peaks at merely 4GB of RAM—my initial thoughts of requiring a complex Kubernetes setup were misguided.

What’s more, when system crashes occur (and they have, twice), I’m equipped with real data on what exactly failed. Spoiler: it’s often not what I anticipated.

2. Directly Hardcoded Configurations

Configurations are explicitly defined within the code, which may seem primitive but has its advantages. By eliminating configurational files or environment variables, I can quickly search for any value using simple grep commands. Additionally, every change is neatly tracked in Git history, and my self-reviews allow for thorough oversight.

Over the past three months, I’ve adjusted configurations only three times. This minimal adjustment time dramatically outweighs the hours it would have taken to develop a dedicated configuration service.

3. Utilizing SQLite in Production

My multi-user web app is powered by a 47MB SQLite database, comfortably handling concurrency with 50 users. Through this approach