The Three-Month Experiment: A Pragmatic Approach to Scalable Solutions

In the world of startups, guidance from thought leaders like Paul Graham often echoes: “Do things that don’t scale.” However, the practical application of this principle in the realm of coding can be elusive. After spending the last eight months developing my AI podcast platform, I’ve crafted a straightforward methodology that I like to call the “three-month rule” for implementing unscalable hacks.

Understanding the Three-Month Rule

As developers, we typically gravitate toward creating scalable systems from the very beginning. This involves incorporating complex design patterns, microservices, and distributed architectures that can handle a massive volume of users. However, in the startup environment, focusing on scalability too early can lead to inefficiencies and unnecessary overhead. My three-month rule allows me to test each hack for a limited time—three months to be exact. If it proves effective, it’s prioritized for a robust solution; if not, it’s phased out.

This approach encourages me to adopt a lean mindset, enabling me to write straightforward, even rudimentary, code that can be deployed quickly. The goal is to learn what users genuinely need and eliminate the guesswork.

Practical Infrastructure Implementations

1. Simplified Architecture: Single VM Utilization

Currently, everything—from the database and web server to background jobs—is housed on a single $40-per-month virtual machine (VM). While this may seem risky with no redundancy or automated backups, this setup has provided invaluable insights.

In just two months, I’ve gained a clearer understanding of my actual resource utilization. For instance, my “AI-heavy” platform peaked at just 4GB of RAM, revealing that the complex Kubernetes structure I considered was unnecessary. When the VM crashes—a scenario I’ve experienced a couple of times—I receive real-time data on what fails, offering surprises that guide my development choices.

2. Hardcoded Constants for Configuration

Instead of relying on configuration files or environment variables, I use hardcoded constants like:

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

Although this setup requires redeployment for any changes, it simplifies the process of tracking updates in version control. Modifying these constants is a quick affair compared to the potential week-long commitment of creating a dedicated configuration service—an effort justified by the