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

In the world of startups, advice from seasoned entrepreneurs often resonates loudly. One of the most quoted pieces of wisdom comes from Paul Graham: “Do things that don’t scale.” This phrase is widely discussed, yet the practical implementation of this concept, particularly in the domain of coding, rarely receives much attention.

Over the past eight months, I’ve been hard at work developing an AI podcast platform, and it has led me to create a straightforward framework structured around a three-month timeline. Each non-scalable solution I devise is given a lifespan of exactly three months. After this period, it must demonstrate its effectiveness or be discarded.

As engineers, we often feel pressured to create solutions that can accommodate scale from the get-go. We’re taught to utilize design patterns, microservices, and distributed systems—magnificent architectures designed to manage millions of users. However, this mindset is typically rooted in big-corporation thinking.

In a startup environment, focusing on scalable solutions can often lead us down a road of expensive procrastination. We find ourselves optimizing for hypothetical users, addressing issues that may never arise. My three-month rule compels me to write simple, direct, and even “messy” code that ships quickly and teaches me about the real needs of my users.

Current Infrastructure Strategies and Their Surprising Benefits

1. Consolidation on a Single Virtual Machine

All of my applications—database, web server, background jobs, and Redis—operate on a single $40-per-month virtual machine. While this approach lacks redundancy, the insights gained have been invaluable. In just two months, I’ve learned more about my actual resource requirements than any elaborate capacity planning document could provide. For instance, my platform, which I presumed would be AI-heavy, typically only requires 4GB of RAM. The complex Kubernetes setup I nearly implemented would have only managed idle containers.

When this system crashes (which has happened twice), I gather concrete data about failures—often in ways I didn’t anticipate.

2. Hardcoded Constants for Configuration

Rather than using configuration files or environment variables, I’ve opted for hardcoded values within my code, such as:

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

This approach allows me to effortlessly