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

In the tech world, we often hear the maxims from industry leaders on what it takes to build a successful product. One of the most resonant pieces of advice from Paul Graham is to “do things that don’t scale.” However, the conversation rarely shifts to implementing this concept in software development practices.

After several months of working on my AI podcast platform—a journey that has lasted eight months—I’ve crafted a straightforward yet effective framework: every unscalable approach is granted a lifespan of three months. Within this period, it must either demonstrate its value and be transitioned into a more robust solution, or face removal.

Rethinking the Need for Scalability

As software engineers, our training often revolves around creating scalable systems from the outset. We’re taught to appreciate design patterns, microservices, and the intricacies of distributed systems—all useful for handling a high volume of users, especially in established companies. However, in a startup environment, pursuing scalability too early can lead to wasted resources and missed learning opportunities.

In my experience, the pressure to create optimally scalable code can often be an excuse for procrastination. By applying my 3-month rule, I’ve shifted my focus towards writing straightforward, albeit imperfect code that allows me to truly understand my users’ needs.

Insights From My Infrastructure Experimentation

Here are some of my current non-scalable, yet effective, hacks and what they’ve taught me:

1. Consolidation on a Single VM
I’ve consolidated everything—database, web server, background jobs, and Redis—onto one $40/month virtual machine. Although it lacks redundancy and relies on local manual backups, this setup has been invaluable. In just two months, I’ve gained insights into my actual resource requirements, discovering that my platform only peaks at 4GB RAM. Rather than investing in a complex Kubernetes architecture that would have led to managing superfluous empty containers, I now have practical data to inform future scaling decisions.

2. Hardcoded Configuration
With all configuration values directly embedded in the code, such as pricing tiers and user limits, I have eliminated the need for separate config files or environment variables. Any modification requires a redeployment, but this simplicity has been beneficial. It allows for rapid search and tracking of changes through version control. In three months, I’ve only changed these values three times, equating to a