Embracing the 3-Month Rule: A Pragmatic Approach to Scalable Development

In the world of startups, the mantra often heard is “do things that don’t scale,” a phrase popularized by Paul Graham. Yet, the challenge lies in how to effectively apply this concept to the realm of coding. After eight months dedicated to developing my AI podcast platform, I’ve devised a straightforward framework that has transformed my approach to coding: every unscalable solution is given a lifespan of just three months. At the end of that period, it either justifies its continued use with proven value or is discarded.

The start-up Mindset: Why Scalability Can Be Misleading

As engineers, we’re conditioned to focus on scalable architectures from the start—this includes utilizing design patterns, microservices, and distributed systems that are engineered to support millions of users. Such thinking is often indicative of larger organizations, whereas in a startup setting, building scalable solutions can frequently lead to unnecessary complications. Often, it feels like we’re procrastinating, developing functionalities and features for users who haven’t even arrived, all while missing out on the insights that come from simpler, more direct coding.

By implementing my three-month rule, I prioritize writing straightforward, sometimes “bad” code that can be deployed quickly. This not only facilitates faster learning but also provides real insights into user needs and platform performance.

My Unconventional Infrastructure Hacks

1. Consolidating to a Single Virtual Machine

Running everything—database, web server, background jobs, and caching—on a single, $40/month virtual machine may seem less than ideal, but this consolidation has proven invaluable. From this setup, I’ve gained insights about my resource needs in two months that no extensive capacity planning document could provide. The reality is that my “AI-heavy” platform only requires a mere 4GB of RAM during peak use. Had I opted for a complex Kubernetes setup, I would have been managing containers that weren’t even necessary.

2. Utilizing Hardcoded Configuration

Instead of extensive configuration files or environment variables, I’ve opted for hardcoded constants in my code:

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

This approach allows me to quickly search for configuration values across my codebase, while any changes are meticulously tracked through git history. I’ve modified these constants