Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development

In the fast-paced world of tech startups, there’s a well-known mantra from entrepreneur Paul Graham: “Do things that don’t scale.” While this principle resonates widely, the execution—especially in technical development—often goes unaddressed. After dedicating the last eight months to developing my AI podcast platform, I’ve implemented a straightforward framework that I call the “3-Month Rule.” This principle allows me to trial non-scalable hacks for a limited time, giving them three months to showcase their value. If they don’t cut it, they’re retired.

As software engineers, we’re typically conditioned to craft scalable solutions right from the start. We immerse ourselves in complex design patterns, microservices, and distributed systems, all built to accommodate massive user bases. However, this mindset can be a trap, especially for startups where resources are scarce and time is precious. In many cases, pursuing scalability upfront can lead to costly delays and unproductive endeavors.

My 3-Month Rule compels me to focus on writing straightforward, albeit unconventional, code that gets shipped quickly. The intent is clear: I want to see what users truly need instead of getting lost in theoretical optimizations for non-existent problems.

Current Hacks: The Power of Simplistic Solutions

1. Consolidated on a Single VM

My entire backend—database, web server, background jobs, and caching—runs on one $40/month virtual machine. It lacks redundancy, and I manage backups manually.

This approach, while seemingly risky, has its advantages. In just two months, I gained insights about resource requirements that no planning document could provide. I discovered that my platform only peaks at 4GB of RAM. The elaborate Kubernetes infrastructure I once considered would have merely managed idle containers.

Each crash (which has occurred twice thus far) taught me invaluable lessons about the real pain points of my codebase—not the ones I initially anticipated.

2. Hardcoded Configuration Variables

With constants like:

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

I have omitted config files and environment variables in favor of straightforward hard-coded values. Any change necessitates a redeployment.

The advantage? I can rapidly search through my codebase for specific values. Each