Embracing the Three-Month Rule: A Practical Approach to Technical Development

In the world of startups, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While this concept resonates with many, the challenge lies in its execution, especially in technology and coding. After eight months of developing my AI podcast platform, I’ve discovered a straightforward framework that I call the “Three-Month Rule.” This approach allows me to give each unscalable solution a trial period of three months. Within this timeframe, a hack must either demonstrate its value and be refined or completely phased out.

The Engineer’s Dilemma

As developers, we often find ourselves inclined to design scalable and intricate solutions right from the outset. We focus on sophisticated architecture such as microservices, distributed systems, and resilient design patterns, all in anticipation of serving millions of users. However, this mentality can lead to expensive delays in a startup environment. More often than not, these well-planned solutions cater to future needs that may never materialize. My Three-Month Rule compels me to adopt a more straightforward approach, creating functional but not necessarily elegant code. This method not only facilitates rapid deployment but also helps me understand the actual requirements of my users through real-world interactions.

Ingenious Hacks That Drive Real Learning

Here are some of the unconventional strategies I’ve employed and the valuable insights I’ve gained from them:

1. Consolidated Operations on a Single Virtual Machine

I currently run my database, web server, background jobs, and Redis on a single $40/month virtual machine. There’s no redundancy, and I handle backups manually. This seeming oversight has actually provided me with invaluable data regarding my resource needs. In two months, I have learned more about my system’s dependencies than any capacity planning document could have revealed. My platform, initially deemed “AI-heavy,” only peaks at 4GB of RAM, meaning that an elaborate Kubernetes setup would have likely resulted in managing idle containers.

2. Simplified Hardcoding of Configuration Values

My configuration is entirely hardcoded, with constants like:

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

This means no configuration files or environment variables. While this may seem primitive, it allows me to quickly search my codebase for any value and meticulously track changes through version control. In just