Embracing the 3-Month Rule: A Pragmatic Approach to Development in Startups

In the world of technology, there’s a well-known piece of advice from Paul Graham that resonates with many entrepreneurs: “Do things that don’t scale.” However, the challenge lies in translating this principle into actionable steps, especially in coding. After eight months of developing my AI podcast platform, I have devised a straightforward framework that I like to call the “3-Month Rule.” This rule guides me in validating unscalable solutions within a time frame, assessing their value, and determining whether to further develop them or phase them out.

Understanding the Challenge of Scalability

As engineers, our training often emphasizes the creation of scalable solutions from the get-go. We become immersed in design patterns, microservices, and distributed systems—all vital for accommodating large user bases. However, this approach can be a mismatch for startups, where crafting scalable solutions too early can lead to wasted resources and unnecessary complexity.

At a startup, focusing excessively on scalability can be tantamount to delaying essential actions. Instead of preemptively optimizing for potential users, I have shifted my focus to writing direct, albeit “imperfect”, code that can be shipped quickly. This not only accelerates the learning process but also reveals what users truly need.

My Current Infrastructure Strategies

Here are several current strategies I’m employing that align with this philosophy—not just as shortcuts, but as smart, pragmatic choices.

1. Unified Virtual Machine

Currently, my entire platform operates on a single $40/month virtual machine (VM) that houses the database, web server, background jobs, and Redis. While this may seem risky due to the lack of redundancy and manual backups, it has provided immense insights. In just two months, I have gained a clear understanding of my resource requirements and have determined that my AI platform peaks at only 4GB of RAM. Had I opted for a complicated Kubernetes setup, I would have struggled with empty containers instead of effectively managing my real needs.

2. Hardcoded Configuration

My configuration is intentionally hardcoded throughout my codebase:

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

While lacking traditional config files may seem inefficient, this approach has its benefits. I can quickly search for any configuration value, track changes through version control, and update