Embracing Imperfection: The 3-Month Rule for Unscalable Solutions

In the startup world, there’s a well-known directive from Paul Graham: “Do things that don’t scale.” While this wisdom is widely recognized, the challenge often lies in its practical application, particularly within the realm of coding. Over the past eight months, while crafting my AI podcast platform, I developed a straightforward approach that I like to call the “3-Month Rule.” This framework forces every unscalable hack into a trial period of three months. After this timeframe, each solution must either demonstrate its worth and undergo a true build-out or be discarded entirely.

The startup Reality Check

As engineers, we often find ourselves trained to create scalable solutions from the outset—embracing design patterns, microservices, and distributed systems in order to support a massive user base. However, in the context of a startup, this can lead to costly delays and unnecessary complexity. Why invest resources into solutions for problems that may never arise? The 3-Month Rule compels me to embrace simple, even “bad” code that is quick to implement, yielding essential insights into user needs before sinking resources into more intricate structures.

My Strategic Infrastructure Hacks

Here are a few of my current infrastructure decisions, which may seem unconventional but serve a purpose:

1. Unified Virtual Machine

I’ve consolidated my entire setup—a database, web server, background jobs, and Redis—into one $40-per-month virtual machine. Admittedly, this approach lacks redundancy and involves manual backups to my local machine. However, within two months, I’ve gleaned more about my actual resource requirements than any capacity planning document could deliver. For instance, I learned that my “AI-heavy” platform only peaks at 4GB of RAM, and the elaborate Kubernetes architecture I nearly implemented would have been managing largely idle containers. When my service crashed—twice—I gained valuable insights about what truly fails, which was often surprising.

2. Hardcoded Configurations

I’ve opted for hardcoded configurations throughout my codebase:

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

While this may seem primitive without config files or environment variables, it allows me to quickly search for values and maintain a complete edit history through Git. Each change prompts a redeployment, a