The 3-Month Rule: A Pragmatic Framework for Unscalable Development

In the world of startup development, the conventional wisdom is often expressed in Paul Graham’s phrase: “Do things that don’t scale.” However, implementing this approach—particularly in coding—can seem elusive. After eight months of building my AI podcast platform, I’ve established a pragmatic framework that I call the 3-Month Rule. This guideline dictates that every unscalable approach I implement will be given a trial period of three months. At the end of this time, it either proves its worth and is refined, or it’s discarded.

The Challenge of Scaling Early

As engineers, we’re trained to prioritize the building of scalable solutions from the outset. We focus on intricate design patterns, microservices, and distributed systems that are capable of accommodating millions of users. However, this mindset often reflects the thinking of larger organizations. In a startup environment, striving for scalability too soon can lead to unnecessary complexities—essentially a form of procrastination. My 3-month rule compels me to prioritize simplicity and directness over theoretical perfection, allowing me to ship code that reveals genuine user needs.

Current Infrastructure Hacks: Smart Choices for Learning

Here are some of the unconventional strategies I’ve employed, which may seem unorthodox but have proven to be valuable learning tools:

1. Unified VM Setup

My entire platform—including the database, web server, background jobs, and caching—operates on a single $40/month virtual machine. There’s no redundancy, and I execute manual backups.

Why is this effective? I’ve gained insights into my actual resource requirements over the past couple of months that no detailed capacity-planning document could provide. Surprisingly, my “AI-loaded” platform has only peaked at 4GB of RAM usage. Had I launched with a complex Kubernetes configuration, I would have been managing idle containers instead of focusing on real-world needs.

2. Simplified Configuration Management

Instead of using configuration files or environmental variables, I’ve hardcoded values directly into the code:

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

Though this means deployments require a little more effort, it brings a hidden advantage: I can quickly search my codebase and track changes through Git history. In three months, I’ve made only three