Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development

In the tech world, the saying often attributed to Paul Graham persists: “Do things that don’t scale.” While this advice resonates on a philosophical level, the practical application—especially in coding—often goes unexplored. After dedicating the past eight months to building an AI podcast platform, I’ve devised a straightforward framework I call the “3-Month Rule.” The core principle is simplicity: any unscalable hack is given a lifespan of just three months. If it proves its worth, it can be properly implemented; if not, it’s time to let it go.

As engineers, we often approach projects with the mentality of creating scalable solutions from the get-go. Concepts like design patterns, microservices, and distributed systems are the pinnacle of architectural planning, seemingly tailored for large companies. However, in a startup environment, chasing scalability too soon frequently leads to wasted time and resources. My 3-month framework compels me to write straightforward, albeit imperfect, code that facilitates rapid learning about users’ true needs.

My Current Infrastructure Hacks: Lessons Learned

1. Single VM Simplicity

Currently, all my services from the database to background jobs run on a single $40-a-month virtual machine. Sounds risky? Perhaps, but it has provided invaluable insights. Within just two months, I learned precisely what resources my platform requires—revealing that at peak performance, 4GB RAM suffices. Instead of managing complex Kubernetes setups, I gained practical experience quickly, especially when the system crashed. Each failure has taught me exactly where risks lie, debunking many preconceived notions.

2. Hardcoded Configurations

Instead of utilizing configuration files, I have hardcoded values throughout my application. For example:

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

Every tweak requires a redeployment, which ironically has become a hidden advantage. By allowing me to search through my entire codebase for configuration values in seconds, I can easily track price changes via Git history. In three months, I’ve modified these constants only a few times, proving that creating an entire configuration service would have been unnecessary.

3. SQLite for Production Use

Interestingly, SQLite serves as my database for a multi-user web