Embracing Imperfection: The 3-Month Rule in Software Development

When it comes to the art and science of software development, the advice from esteemed entrepreneur Paul Graham still rings true: “Do things that don’t scale.” Yet, the conversation often overlooks the practical implementation of this principle within coding workflows.

Over the past eight months, I have been developing my AI podcast platform, and throughout this journey, I’ve created a simple yet effective framework known as the “3-Month Rule.” This approach allows each unscalable hack a trial period of three months. Following that timeframe, each hack must either demonstrate its value and be integrated into a robust solution or be discarded.

It’s commonplace for engineers to aim for scalable solutions right from the outset. We often find ourselves engrossed in intricate architectures—microsystems, distributed systems, and processes designed to accommodate millions of users. However, this is typically the mindset of larger corporations, and in a startup setting, striving for immediate scalability can lead to costly delays. The reality is that you’re often optimizing for nonexistent users and addressing challenges that may never arise. The 3-Month Rule encourages me to focus on creating straightforward, functional code that can be shipped promptly, providing me with genuine insights into user needs.

Current Infrastructure Strategies: Practical Lessons Learned

1. Centralized Virtual Machine Usage

I operate everything from a single $40 per month virtual machine, hosting the database, web server, background jobs, and even Redis—all under one roof. While this setup lacks redundancy, it has imparted invaluable lessons regarding my actual resource requirements. In just two months, I’ve grasped that my AI-centric platform only requires a peak of 4GB of RAM. The elaborate Kubernetes architecture I considered? It would have only managed unused resources.

When I experience crashes (a couple of times so far), I gain concrete insights into what fails—often revealing surprises.

2. Streamlined Code Configuration

I employ hardcoded values throughout my codebase for configurations, such as pricing tiers and user limits:

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

While it might seem rudimentary to forgo configuration files and environment variables, the simplicity allows me to quickly search through my code for any particular value. This means that every change in pricing is