Embracing the 3-Month Rule: A Practical Approach to Learning Through Unscalable Solutions

In the world of startups, it’s common wisdom to embrace the mantra: “Do things that don’t scale.” While advice from tech luminaries like Paul Graham is popular, the nuts-and-bolts of putting this into practice in the realm of software development often go unexplored. During my eight-month journey of building an AI podcast platform, I stumbled upon a straightforward framework that I like to call the “3-Month Rule.” This rule dictates that any unscalable hack I implement is given a trial period of three months. By the end of this timeframe, it must either validate its worth and be refined into a robust solution, or fade away—ensuring focused resource allocation on what truly matters.

As engineers, we often have the inclination to aim for scalable solutions right from the outset. Many of us are trained to create sophisticated design patterns, leverage microservices, and optimize for distributed systems—all essential for managing vast user bases. However, this forward-thinking architecture is typically a strategy for larger organizations. In a startup environment, attempting to scale prematurely can lead to needless complexity and resource waste by planning for users who are not yet onboard.

The 3-Month Rule forces me to write simpler, more direct code—some might even say “bad” code—that is functional and available to users. This approach has allowed me to gain profound insights into the actual needs and behaviors of my users.

Current Infrastructure Hacks: Simplifying for Clarity

1. Single VM Deployment

At the moment, I’m operating everything on a single virtual machine that costs just $40 a month. This setup includes my database, web server, background jobs, and Redis—all contained in one place without redundancy. Though it may seem reckless, this simplicity has been enlightening. I have gathered more accurate data about my resource needs in two short months than I ever would have from extensive capacity-planning documents. Surprisingly, my platform only requires around 4GB of RAM, making elaborate containerization setups unnecessary—saving me from the hassle of managing unutilized resources.

2. Hardcoded Configuration Settings

In my codebase, configurations are hardcoded constants instead of stored in separate files or as environment variables. Each change necessitates a redeployment, but the efficiency is remarkable. I can quickly search for any configuration value across my entire codebase. Since I’ve made only a handful of changes