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

In the dynamic world of startups, the advice “do things that don’t scale,” famously championed by Paul Graham, is often easier said than done—especially when it comes to coding. After dedicating eight months to developing my AI podcast platform, I stumbled upon an effective strategy: the 3-Month Rule. This framework allows each unscalable innovation to thrive for exactly three months. If it proves its worth, it gets upgraded; if not, it’s time to let go.

The Challenge of Scalability in Startups

As software engineers, we are often conditioned to aim for scalable solutions from the outset. We dream of sophisticated design patterns, microservices, and architectures that can accommodate millions of users. However, this mindset can be detrimental in the startup environment, where building a scalable infrastructure too early can lead to wasted resources and procrastination. It’s like preparing a feast for guests who haven’t even RSVP’d yet.

My 3-Month Rule encourages me to focus on simplicity and practicality in my coding approach. Rather than getting bogged down in complex, “perfect” code, I prioritize straightforward solutions that can quickly provide insights into the needs of my actual users.

Innovative Infrastructure Hacks: Why They Work

1. Single Virtual Machine Setup

Currently, everything from my database to the web server runs on one $40/month virtual machine, with no redundancy and manual backups to my local system. This approach has been eye-opening—I’ve gained more insight into my resource requirements in just two months than I would have from any formal capacity planning document. It turns out that my AI platform only uses 4GB of RAM at peak times, rendering my more intricate Kubernetes plans irrelevant.

When system failures occur (which have happened a couple of times), they provide valuable lessons about what actually goes wrong—insights that are often surprising.

2. Hardcoded Values—The Hidden Gem

Instead of complex configuration files, I have hardcoded constants such as:

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

While this may seem primitive, it allows me to trace any configuration changes quickly and track them in version history. Redeploying these minor adjustments takes mere minutes instead of consuming precious engineering hours.

3. Using SQLite in Production

Running SQLite for a multi-user web application