Embracing the 3-Month Rule: A Pragmatic Approach to Development in Startups
In the ever-evolving tech landscape, one piece of advice consistently shines through: “Do things that don’t scale.” This wisdom, championed by Paul Graham, serves as a guiding principle for entrepreneurs. However, the challenge often lies in translating this advice into actionable coding strategies in a startup environment.
Over the past eight months of developing my AI podcast platform, I’ve crafted a straightforward framework that has transformed my approach to unscalable solutions. I call it the 3-Month Rule: any technical workaround is given a lifespan of exactly three months; at the end of this period, it either proves its worth and gets a legitimate upgrade or it gets phased out.
The Problem with “Scalable” Thinking
As engineers, we frequently gravitate toward scalable architectures from the outset—design patterns, microservices, distributed systems—and while such setups can efficiently manage vast user bases, they often represent the mentality of larger corporations. In a startup, focusing on scalability too early can lead to excessive resource allocation for potential future users that may never materialize. My 3-Month Rule encourages me to prioritize rapid deployment with straightforward solutions that genuinely reflect user needs.
My Current Tactical Infrastructure and Their Strategic Value
1. One VM for All Operations
I’m running a comprehensive setup on a single $40/month virtual machine encompassing my database, web server, background jobs, and caching. With no redundancy and manual backups to my local system, this approach may initially seem reckless.
However, this hyper-focused setup has yielded invaluable insights into my actual resource demands. After two months, I realized that my “AI-intensive” platform only requires 4GB of RAM—far less than the complex Kubernetes cluster I almost built. Moreover, during the few crashes I faced, I gained concrete data on what truly fails, which was often unexpected.
2. Hardcoded Configuration Values
As an example, my pricing and user caps are hardcoded directly into the codebase:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This may seem unorthodox, yet it allows for rapid configuration changes with minimal overhead. Each change is easily trackable in version control, and I found that I’ve only needed to modify these values
