Embracing the 3-Month Rule: A Practical Approach to Rapid Development in Startups

In the startup landscape, Paul Graham’s notion of “doing things that don’t scale” is a guiding principle often discussed but rarely implemented effectively, especially in the realm of coding. After eight months of developing my AI podcast platform, I’ve adopted a unique framework that I call the “3-Month Rule.” This approach allows each unscalable hack to thrive for three months, after which it either transitions into a fully-fledged solution or is promptly discarded.

The Importance of Iteration Over Perfection

As engineers, our instincts often drive us to create scalable solutions from the onset. We envision robust architectures with microservices and distributed systems that can handle thousands of users. While this is essential for major corporations, it can lead to unnecessary complexity in a startup’s early stages. Often, striving for scalability can be akin to procrastinating—optimizing for hypothetical users while our existing product struggles to take flight.

The 3-Month Rule challenges me to prioritize straightforward, albeit less elegant, coding strategies that encourage rapid deployment and genuine user feedback. Here’s a closer look at some of my current infrastructure choices and the rationality behind them.

1. Single VM for All Operations

My entire setup—database, web server, background jobs, and caching—is hosted on a single, cost-effective $40/month virtual machine. While it lacks redundancy, this streamlined approach has provided invaluable insights into my resource needs within just two months. I’ve discovered that, contrary to my initial beliefs, my AI-driven platform only requires about 4GB of RAM at peak usage. The intricate Kubernetes configuration I once contemplated would have simply been managing dormant resources.

When crashes occur, which they have a couple of times, I’m able to gather invaluable data about system failures that reflect the actual user experience, often revealing surprises.

2. Directly Hardcoded Configurations

Instead of employing config files or environment variables, I use hardcoded constants throughout my codebase:

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

While this may seem primitive, it allows for rapid changes with low overhead—just redeploy and move on. I can quickly search my entire codebase for configuration values, and each price adjustment is logged in git history, subject to my oversight. Over