The 3-Month Rule: A Practical Approach to Implementing Non-Scalable Solutions in Software Development

In the world of software development, the advice from Paul Graham to “do things that don’t scale” is often quoted but seldom unpacked, especially in the context of coding practices. After dedicating eight months to building my AI podcast platform, I have crafted a straightforward framework that I call the “3-Month Rule.” This principle dictates that any non-scalable workaround is given a trial period of three months to prove its worth—after which we either scale it up appropriately or discard it altogether.

Typically, as engineers, we are conditioned to focus on creating scalable solutions from the outset. We think in terms of design patterns, microservices, and distributed systems, which are designed to handle vast user bases. However, this approach often resembles more of a strategy for larger firms than for burgeoning startups. In these early stages, investing time in scalability can sometimes be nothing more than a deferred decision. The 3-Month Rule compels me to write straightforward, albeit imperfect, code that can be deployed quickly, allowing me to engage directly with user behaviors and needs.

Key Infrastructure Approaches and Their Strategic Benefits

1. Consolidated Resources on a Single VM

All aspects of my platform—including the database, web server, background jobs, and caching—are hosted on one $40/month virtual machine. This setup has zero redundancy, and I rely on manual backups to my local system.

The brilliance of this choice lies in its practicality: over the past two months, I’ve gained clearer insights into my actual resource requirements than I would have through any extensive capacity planning document. For instance, my platform, which I initially expected would require significant resources, sees usage peaks at only 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have been wasted on managing idle containers. The crashes I’ve experienced have also provided invaluable data, revealing unexpected points of failure.

2. Hardcoded Configurations

All settings are coded directly into my application:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

There are no configuration files or environment variables, and updating any parameter necessitates a redeployment. This approach, while seemingly clunky, has its advantages. With an extensive codebase, I can quickly search for any setting,