Embracing the 3-Month Rule: A Pragmatic Approach to Building an Unscalable Product

In the startup world, one piece of sage advice frequently shared among founders and developers alike is to “do things that don’t scale.” While Paul Graham’s wisdom resonates, the nuances of applying this principle in the tech realm often go unaddressed. Over the past eight months, while developing my AI podcast platform, I have crafted a straightforward framework known as the “3-Month Rule.” This approach allows unscalable solutions to thrive temporarily, and after a three-month testing period, they either evolve into robust systems or are retired.

The Engineer’s Dilemma: Pursuing Scalability Too Soon

As software engineers, we often gravitate towards building scalable systems right from the beginning. We focus on architectural best practices, employing design patterns and embracing microservices to ensure our software can handle millions of users. However, this is a mindset more fitting for established corporations than for budding startups.

In a startup environment, chasing scalability too early can be a costly distraction. It can lead to resources being allocated to problems that might not even exist yet. My 3-Month Rule empowers me to write straightforward, sometimes “imperfect” code that delivers results and elucidates the actual needs of my users.

Examples of My Pragmatic Infrastructure

Here, I will share some of my current hacks and how they are thoughtfully applied:

1. Consolidated Hosting: One VM to Rule Them All

I utilize a single virtual machine (VM) for my database, web server, background tasks, and caching systems, all for a minimal cost of $40 per month. While this approach has zero redundancy and relies on manual backups, it has provided invaluable insights. In just two months, I’ve gathered more data regarding my resource requirements than any theoretical capacity planning document could have revealed. I’ve discovered that my “AI-heavy” platform peaks at only 4GB of RAM, indicating that an intricate setup like Kubernetes would have been overengineered.

2. Hardcoded Configurations: A Simpler Approach

Instead of relying on configuration files and environment variables, I’ve opted to use hardcoded constants for essential values like pricing tiers and user limits. While this may seem cumbersome at first glance, I can quickly search my entire codebase for any configuration value using simple commands. This method has allowed for rapid iterations with minimal overhead—an elaborate configuration service would have taken far longer to implement,