Embracing the 3-Month Rule: A Practical Approach to Building Scalable Solutions

In the realm of startup development, there’s a well-known piece of advice from entrepreneur Paul Graham: “Do things that don’t scale.” However, many developers often overlook how to effectively apply this mindset to their coding practices. After eight months of developing my AI podcast platform, I’ve devised a straightforward methodology: any solution that lacks scalability receives a trial period of three months. If it demonstrates value within that timeframe, it gets a robust implementation; if not, it’s retired.

As engineers, we’re often trained to prioritize scalability from the very start. We think in terms of design patterns, microservices, and distributed systems—architectures that can accommodate millions of users. However, this perspective can lead to what I call “big company thinking.”

In the early stages of a startup, obsessing over scalable code can turn into a costly delay, often dedicated to addressing potential issues that may never arise. The 3-month rule keeps me focused on crafting direct, sometimes imperfect code that can be deployed quickly, allowing me to truly understand my users’ needs.

Current Infrastructure Innovations and Their Strategic Value

1. Consolidated into a Single VM

All components of my platform—including the database, web server, background tasks, and caching—reside on a single $40/month virtual machine. This setup comes with no redundancy and relies on manual backups to my local machine.

Why is this an intelligent choice? In just two months, I’ve gained more insights about my resource utilization than any extensive capacity planning report could provide. I discovered that my platform typically operates at around 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have involved managing idle containers instead of serving actual users.

When failures occur (and they have, twice), I gain real-time feedback about the underlying issues—surprisingly, they aren’t what I anticipated.

2. Hardcoded Configurations

Instead of using configuration files or environment variables, my code contains hardcoded constants:

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

This may seem impractical, but it has a hidden advantage: I can easily search through my entire codebase for any configuration value in a matter of seconds. Moreover, every configuration adjustment is included in my git history and