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

When it comes to startup culture, the mantra “Do things that don’t scale,” popularized by Paul Graham, is a common point of discussion. However, I have found that the challenge lies not just in understanding this philosophy but effectively incorporating it into a technical framework. Over the past eight months of developing my AI podcast platform, I’ve devised a strategy that I call the “3-Month Rule.” Simply put, any non-scalable hack gets a three-month trial period. At the end of this timeframe, it must either prove its worth and be fully implemented, or be discarded.

As engineers, we are often conditioned to prioritize scalable solutions from the get-go. This typically translates to employing design patterns, microservices, and distributed systems capable of handling millions of users. However, such thinking may not serve a startup well. In fact, scalable code can often mean expensive procrastination—optimizing for potential users who may never exist and resolving issues that may never arise. The 3-Month Rule encourages me to focus on creating straightforward, though perhaps less-than-ideal, code that can be deployed quickly and expose real user needs.

Current Infrastructure Strategies: Smart Hacks That Work

Here are some of the unconventional yet effective strategies I’ve adopted in my project:

1. Consolidated Environment on a Single Virtual Machine

Currently, my database, web server, background jobs, and caching are all running on one $40/month VM. Although it lacks redundancy and relies on manual backups to my local machine, this approach has been enlightening. Within two months, I’ve gained more insight into my actual resource needs than any capacity planning document could offer. For instance, I discovered that my platform’s peak usage is only 4GB of RAM. The complex Kubernetes setup I almost deployed would have required managing virtually empty containers.

When my system crashes—twice, to date—I’m able to gather immediate, valuable data on the causes. Surprisingly, the failures have always differed from my predictions.

2. Hardcoded Configuration for Simplicity

Instead of relying on configuration files or environment variables, I’ve hardcoded key parameters directly into the code. My constants look something like this:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The beauty of this