Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the world of startup development, the often-quoted wisdom from Paul Graham resonates: “Do things that don’t scale.” However, many overlook the practical implementation of this strategy, especially when it comes to coding. As I’ve navigated the complexities of building my AI podcast platform over the past eight months, I’ve created a straightforward framework: every unscalable hack gets a lifespan of three months. After this period, it will either demonstrate its effectiveness and be enhanced, or it will be retired.

As engineers, we often feel the pressure to construct scalable solutions from the outset—design patterns, microservices, and intricate architectures that are designed to handle vast user bases. This tendency is largely a mindset cultivated by larger organizations. In a startup environment, pursuing scalable code can lead to delays and unnecessary complication, fixing issues for non-existent users. My 3-month rule compels me to write straightforward, albeit “imperfect,” code that can be deployed quickly, enabling me to truly understand my users’ needs.

My Smart Infrastructure Hacks

1. Simplified Single-VM Architecture

I host my entire platform—database, web server, background jobs, and Redis—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided valuable insights. Within just two months, I’ve gained a clearer understanding of my actual resource requirements, debunking the need for an elaborate Kubernetes environment. When issues arise (and they have), I can pinpoint what truly fails—often against my original assumptions.

2. Hardcoded Configurations

Instead of utilizing configuration files or environmental variables, I’ve opted for hardcoded constants spread throughout the codebase, such as pricing tiers and user limits. Changing a configuration requires a simple redeployment, but the benefits are significant. I can quickly search my entire codebase for any config value, and each adjustment is meticulously tracked in version history. Building a configuration management system would have consumed a week of development time. Instead, I’ve spent about 15 minutes on three updates in three months.

3. Leveraging SQLite for Production

Surprisingly, I am using SQLite for my multi-user application, which has a modest database size of just 47MB. It efficiently handles 50 concurrent users with ease. This choice stemmed from analyzing my access patterns, which revealed that my workload is predominantly read