Embracing the 3-Month Rule: A Practical Framework for Building Unscalable Solutions

In the entrepreneurial landscape, the wisdom of renowned startup investor Paul Graham resonates loudly: “Do things that don’t scale.” Yet, the approach to effectively implement this ideal in the realm of coding often goes unaddressed. After dedicating the last eight months to developing my AI podcast platform, I’ve devised a straightforward framework that centers around a fundamental principle: each unscalable hack receives a lifespan of precisely three months. At the end of this period, solutions either demonstrate their worth and are refined, or they are discontinued.

As engineers, we frequently fall into the trap of designing for scalability from the outset—think complex architectures, microservices, and distributed systems meant to accommodate millions of users. While such thinking is essential for larger corporations, it can lead to wasted resources in a startup environment. Opting for scalable solutions prematurely can often feel like an expensive form of procrastination. My three-month rule encourages me to write simple, straightforward code that prioritizes swift deployment and, critically, offers real insights into user needs.

My Current Infrastructure Hacks: Simplicity is Key

1. Unified Operations on a Single VM

I run everything—database, web server, background jobs, and caching—on a single $40/month virtual machine. I sacrifice redundancy and manage backups by hand.

Why is this a wise choice? In just two months, I’ve gained a better understanding of my actual resource demands than any comprehensive capacity planning document could offer. The result? I’ve discovered that my “AI-heavy” platform only utilizes about 4GB of RAM. The complex Kubernetes setup that I nearly initiated would have resulted in empty containers requiring management.

When the server crashes, which has occurred twice, I receive invaluable data about the actual weaknesses in my system—factors that often surprise me.

2. Hardcoded Configuration

Take a look at my configuration:

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

I rely solely on hardcoded constants throughout the codebase, negating the need for config files or environment variables. Each modification requires a redeployment.

The hidden advantage? I can quickly search for any configuration value in seconds. All changes are recorded in git history and subjected to code reviews—albeit my own—but it’s still