Embracing the 3-Month Rule: A Practical Approach to Early-Stage Development

In the tech world, the phrase “do things that don’t scale” is often touted as a key strategy for startups. While established entrepreneurs like Paul Graham champion this philosophy, few delve into the nitty-gritty of how to effectively implement it in software development. After spending eight months developing my AI podcast platform, I’ve crafted a technical framework that I like to call the “3-Month Rule.” This approach guarantees that every unscalable hack I implement is given a three-month trial. If it proves its worth within that timeframe, I will develop it further; if not, it gets the boot.

As engineers, we typically aim to build scalable solutions right off the bat, gravitating towards complex architectures like microservices and distributed systems meant for accommodating vast user bases. However, within a startup context, this often results in spending time and resources on optimizations for hypothetical users and problems that may never arise. My approach compels me to write straightforward, albeit imperfect, code that actually launches and reveals the true needs of my users.

Here’s a look at some of my current infrastructure hacks and the reasons why I believe they are more insightful than they may initially appear:

1. Everything Runs on a Single Virtual Machine

Currently, my database, web server, background jobs, and Redis are all hosted on an economical $40/month virtual machine. Yes, it lacks redundancy, and I perform manual backups to my local computer. The benefit? In just two months, I’ve gained more practical insights into my resource requirements than any theoretical capacity planning document could offer. Surprisingly, my AI-oriented platform peaks at just 4GB of RAM. Had I set up a complex Kubernetes architecture, I would have been juggling empty containers instead of addressing actual usage patterns. Plus, each crash provides me with concrete data about what goes wrong—information that frequently contradicts my expectations.

2. Hardcoded Configuration Values

Instead of using configuration files or environment variables, my code utilizes hardcoded constants for settings such as pricing and maximum users. For instance:

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

This method may seem primitive, but it has its advantages. I can quickly search my entire codebase for any configuration value, and I