Embracing the 3-Month Rule: A Framework for Effective Experimentation in Tech Startups

In the world of software development, renowned entrepreneur Paul Graham has famously suggested that we should “do things that don’t scale.” However, the implementation of this principle within the coding realm often goes unaddressed. After dedicating eight months to constructing my AI podcast platform, I’ve created a straightforward framework I call the “3-Month Rule.” Under this rule, each unscalable approach I try is given a limited lifespan of three months. At the end of this period, I evaluate whether it deserves further investment or if it should be retired.

As developers, we often feel pressured to craft solutions that are scalable from the outset—think microservices, distributed systems, and intricate architectures designed to handle immense user loads. While these concepts are essential for larger companies, they can be impractical for startups. In many cases, focusing on scalability prematurely only leads to costly delays and inefficiencies, as we may be optimizing for problems that have yet to materialize.

The 3-Month Rule compels me to pursue simpler, more direct coding methods that deliver tangible results. Here’s a glimpse into the infrastructure hacks I’m utilizing, which may seem unconventional but have proven remarkably effective.

1. Consolidation on a Single Virtual Machine

Currently, my entire infrastructure—including the database, web server, background processes, and caching—is hosted on a single $40/month virtual machine. This approach has its risks; there’s no redundancy, and I perform manual backups to my local machine. However, this choice has allowed me to gain unparalleled insights into my resource requirements. In just two months, I learned that my “AI-heavy” platform peaks at only 4GB of RAM. I realized that a complex Kubernetes setup would have merely been managing idle resources while not addressing actual issues.

When the system encounters a crash (which has happened twice), I receive genuine feedback on what fails—and the results are often surprising.

2. Hardcoded Configuration

My configuration is hardcoded directly within the codebase, with constants like:

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

While there are no configuration files or environment variables, the benefit is clear: I can quickly search through my code for any configuration value. Each price change is documented