The Three-Month Rule: My Proven Framework for Embracing Non-Scalable Solutions in Tech

In the world of tech startups, the adage from Paul Graham, “Do things that don’t scale,” is widely acknowledged. However, the conversation often omits practical guidance on how to implement this advice effectively in coding endeavors.

After eight months of developing my AI podcast platform, I’ve crafted a straightforward yet impactful framework: every unscalable workaround is granted a lifespan of three months. At the end of this period, the solution needs to either demonstrate its value and receive a proper implementation or be phased out.

As engineers, we are typically conditioned to pursue scalable solutions from the outset. We dig into design patterns, microservices, and intricate architectures intended to support millions of users. While this approach is crucial for larger corporations, in the startup environment, focusing on scalability too soon can become an expensive exercise in procrastination. My three-month rule compels me to write straightforward and sometimes “imperfect” code that facilitates actual shipping of the product while revealing genuine user needs.

Current Infrastructure Strategies That Work Wonders

1. Centralized Operation on a Single Virtual Machine

Everything from the database to the web server operates on a single, cost-effective $40/month VM. There’s no redundancy, and I conduct manual backups locally.

Why is this approach effective? In just two months, I’ve gained a clearer understanding of my actual resource requirements than any comprehensive capacity planning document could provide. I discovered that my “AI-heavy” platform peaks at 4GB of RAM—something I’d have missed while managing an elaborate Kubernetes setup.

When crashes occur (which they have, twice), I’m armed with firsthand data on what actually fails—often surprising me in the process.

2. Hardcoded Configuration for Simplicity

I have constants written directly into the code, such as:

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

While this means no dedicated config files or environment variables, it allows for lightning-fast access to configuration changes. Any modification requires a simple redeploy, and I can swiftly track changes through Git history.

Creating a dedicated configuration service would have consumed a week of my time. Yet, I’ve only changed these values three times over three months—totaling an investment of just 15 minutes rather than 40