Embracing the 3-Month Rule: A Pragmatic Approach to Building Scalable Software
In the world of software development, one piece of wisdom stands out: Paul Graham’s insightful directive to “do things that don’t scale.” While the mantra resonates deeply with entrepreneurs and startups, the implementation often remains uncharted territory—especially for engineers.
Throughout my journey of creating an AI podcast platform over the past eight months, I’ve cultivated a unique approach to navigating this challenge. I call it the 3-Month Rule: every temporary, unscalable solution is given a lifespan of three months. After that period, we evaluate its effectiveness. If it has demonstrated its worth, we invest in refining it; if not, it’s time to let it go.
The Reality of startup Development
As engineers, we are typically conditioned to seek scalable solutions from the outset. Concepts like design patterns, microservices, and distributed architectures are essential for larger organizations, but these frameworks can sometimes lead to costly delays in a startup setting. We risk investing heavily in solutions that might cater to non-existent users or unneeded features. The core of my 3-month philosophy is to encourage rapid learning through the creation of straightforward, if imperfect, code. Here’s a look at some of the infrastructure hacks that illustrate this idea:
1. Consolidated Operations on a Single Virtual Machine
My approach involves running everything—database, web server, background jobs, and caching—on a single $40-per-month virtual machine. While this may sound risky with its lack of redundancy, the real genius lies in the insights I’ve gained about my resource needs. In just two months, I’ve discovered that my AI-centric platform peaks at a mere 4GB of RAM, saving me from unnecessary complexity. Each crash (two so far) provides valuable data about failures that I could not have anticipated.
2. Hardcoded Values Across the Codebase
Instead of maintaining complex configuration files or environment variables, I rely on simple constants:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method offers the advantage of easily tracking changes through version control, and the simplicity allows for rapid redeployment when needed. Within the past three months, I’ve made only three adjustments, which took a mere 15 minutes compared to the substantial time
