The 3-Month Experiment: A Pragmatic Approach to Unscalable Solutions in Development

When it comes to the startup scene, there’s a widely recognized piece of wisdom from Paul Graham: “Do things that don’t scale.” Unfortunately, the implementation of this principle in the coding realm often gets overlooked. After spending eight months focused on developing my AI podcast platform, I discovered a practical methodology that I’ve dubbed the “3-Month Rule.”

This rule allows me to test out unscalable strategies for a maximum of three months. At the end of this period, I carefully evaluate each strategy’s impact; if it demonstrates value, it gets developed into a robust feature—if not, it’s time to let it go.

Rethinking Scalability in Startups

As engineers, we’re conditioned to prioritize scalability from day one. We delve into the realms of microservices and intricate architecture designed to accommodate millions of users. Yet, this kind of thinking can be misguided in a startup scenario where developing scalable solutions too early can amount to a costly distraction.

Instead of crafting an elaborate setup for hypothetical users, my 3-Month Rule encourages me to embrace simpler, more direct coding practices. The aim isn’t perfection; it’s about shipping real products and gaining genuine insights into user needs.

Current Infrastructure Hacks: Why They Make Sense

1. Consolidating Everything to One VM

Everything from the database to background jobs operates on a single, budget-friendly virtual machine costing only $40 a month, without any redundancy. While this setup may appear simplistic, it has granted me invaluable data about my resource requirements in just two months.

With a peak memory usage of 4GB for my “AI-heavy” platform, I’ve avoided the complexities of an intricate Kubernetes setup, focusing instead on real metrics captured during system crashes.

2. Hardcoded Configurations for Agility

My approach includes hardcoded constants like pricing tiers and user limits, bypassing the need for external configuration files. This method may seem archaic, but it has vastly simplified my workflow. If I need to adjust anything, it involves a quick redeployment, saving weeks of development time. Perhaps more importantly, tracking changes through version control guarantees that every modification is accounted for.

3. Utilizing SQLite in Production

In a rather unconventional choice, I opted for SQLite to manage my multi-user application, a decision that has proven effective. My database is a mere 47MB, effectively handling concurrent users