The 3-Month Rule: A Practical Approach for Building Non-Scalable Solutions

In the entrepreneurial landscape, advice from industry experts often resonates deeply. Take Paul Graham’s well-known recommendation to “do things that don’t scale.” Universally applicable as it is, the nuances of implementing this insight, particularly in software development, are seldom discussed.

After eight months of building my AI-focused podcast platform, I have adopted an insightful framework: every unscalable method I use has a lifespan of precisely three months. At the end of this period, it either demonstrates its value, leading to a more robust iteration, or it is discarded altogether.

As software engineers, our instinct is to construct solutions that are scalable from the outset—architecting intricate systems designed for high-capacity usage. This mindset, while valid for larger corporations, can often lead to costly delays in the startup environment. The truth is, writing scalable code too soon can become a form of procrastination, focusing on hypothetical users and problems that may never materialize. My three-month rule compels me to prioritize simplicity and rapid deployment, creating straightforward, albeit “imperfect,” solutions that ultimately inform me about genuine user needs.

Current Infrastructure Strategies: Smart Hacks for Genuine Insights

1. Consolidation on a Single VM

Currently, my entire infrastructure—including the database, web server, background jobs, and caching—is operating on a single virtual machine costing $40 a month. There’s no redundancy and backups are done manually to my local system.

This approach has proven valuable; in just two months, I gained clearer insights into my actual resource demands than any expansive planning document could provide. My so-called “AI-heavy” platform typically utilizes a mere 4GB of RAM. The rather complex Kubernetes architecture I had considered implementing would have only served to manage unnecessary empty containers. Each time the server experiences a crash—twice so far—I gain invaluable data about unexpected failure points.

2. Configuration Hardcoding

Instead of employing config files or environment variables, my setup comprises hardcoded constants across files, such as:

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

This decision, while seemingly simplistic, allows me to quickly search my entire codebase for any specific configuration value. Changes are tracked in Git history, and every update goes through my personal