Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Development
In the ever-evolving landscape of startup culture, the advice from tech luminary Paul Graham resonates with founders and engineers alike: “Do things that don’t scale.” However, navigating this concept in the world of coding often remains uncharted territory. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework to make this principle a reality: I give every hack that defies scalability a three-month lifespan. After this period, I assess the hack’s impact—if it proves valuable, it gets a robust implementation; if not, it’s time to let it go.
As engineers, we often feel compelled to architect scalable solutions right from the get-go—envisioning grand designs like microservices and distributed systems that can handle millions of users. While this approach is typical in larger companies, it can lead to unnecessary complexity in a startup environment. I’ve learned that scalable solutions can sometimes be a means of avoiding immediate challenges and that my three-month rule encourages me to prioritize developing straightforward, albeit imperfect, code that provides real insights into user needs.
My Current Infrastructure Hacks: A Smart Approach
1. Consolidated Operations on a Single VM
With my entire stack—database, web server, background jobs, and Redis—all running on a single $40-per-month VM, I embrace operational simplicity. Though it may lack redundancy, this setup allowed me to gain invaluable insights regarding my resource needs within just two months, revelations that no elaborate planning document could offer. I discovered that my AI platform only peaks at 4GB of RAM, rendering my ambitious plans for a Kubernetes architecture unnecessary. Each crash teaches me what truly needs tweaking, often revealing unexpected factors at play.
2. Directly Hardcoded Values
Instead of employing configuration files or environment variables, I’ve hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach may seem simplistic, but it holds a unique advantage. I can swiftly locate any configuration value through a quick search and track adjustments in version control. Over three months, I’ve made only three changes, which took 15 minutes to redeploy versus the extensive hours required for a formal configuration service.
