Embracing the 3-Month Rule: A Practical Framework for Unscalable Solutions in Tech Development
In the startup environment, agility and adaptability are paramount. Renowned entrepreneur Paul Graham once advised entrepreneurs to “do things that don’t scale.” Yet, implementing this advice, particularly in software development, remains a topic often overlooked. After eight months of developing my AI podcast platform, I’ve crafted a straightforward methodology: every unscalable technique I employ is allocated a lifespan of three months. During this period, it must either demonstrate its worth and transition into a fully developed feature or be discarded.
As engineers, we tend to gravitate towards creating scalable solutions from the outset. The allure of implementing sophisticated design patterns, microservices, and distributed systems can be strong, especially when we envision managing a vast user base. However, this approach often loses sight of the present needs of a burgeoning startup.
Instead, I propose that early-stage projects require practical coding that prioritizes current user needs over theoretical future capacity. My three-month rule allows me to create straightforward, sometimes “imperfect,” code that can be deployed quickly, enabling me to gather insights about user behavior and preferences.
Current Strategies: Unconventional Yet Effective
1. Unified VM Infrastructure
I have consolidated all components—database, web server, background processes, and cache—onto a single $40/month virtual machine (VM). Although this setup has no redundancy and involves manual data backups, the benefits have been invaluable. In just two months, I’ve gained critical insights into my resource demands, confirming that my platform’s peak usage requires only 4GB of RAM. The elaborate Kubernetes configuration I was contemplating would have been an exercise in overkill, managing resources that I don’t need.
During the few instances of downtime, I have been able to discern the precise causes of failures—often surprising and different from my initial expectations.
2. Simplified Code Configuration
Every configuration setting in my project exists as hardcoded constants, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While there are no configuration files or environment variables, this approach allows me to search through the entire codebase for any parameter in seconds. Any changes necessitate a redeployment, which has occurred only three times in three months. This method has saved countless hours of engineering work for the
