Embracing the 3-Month Rule: A Practical Framework for Iterative Development

In the world of entrepreneurship, we often hear the mantra, “Do things that don’t scale,” famously articulated by Paul Graham. However, few delve into how to effectively incorporate this philosophy into the realm of software development. After eight months of developing my AI podcast platform, I’ve established a method that I like to refer to as the “3-Month Rule.” This framework allows every unscalable solution a trial period of three months. Following this period, these solutions either prove their worth and transform into robust systems or they are deemed unnecessary and discarded.

As engineers, we’re conditioned to focus on creating scalable systems from the get-go—emphasizing design patterns, microservices, and distributed architectures that can accommodate millions of users. But in the fast-paced startup environment, chasing scalability too soon can be a costly form of procrastination. It’s like optimizing for a user base that doesn’t yet exist while ignoring the needs of current users. My 3-Month Rule encourages me to write straightforward, even “subpar,” code that is deployable and reveals genuine user requirements.

Unpacking My Current Implementation Strategies

1. Unified Virtual Machine

Currently, everything on my platform runs on a single $40/month virtual machine. This includes the database, web server, background processing, and caching, all without redundancy or automated backups.

Contrary to what some might think, this setup has been highly informative. In just two months, I’ve gained a clearer understanding of my actual resource demands, realizing that my supposedly “heavy” AI platform only needs around 4GB of RAM. The Kubernetes architecture I nearly implemented would have been wasted on managing otherwise idle containers. Each time the system crashes (which has happened twice), I gain real insight into unexpected failure points, which ultimately informs better future decisions.

2. Directly Hardcoded Settings

My configuration files are noticeably absent. Instead, constants such as:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
are hardcoded throughout the application. This practice allows me to locate configuration values within seconds and ensures that every change is logged and reviewed. While implementing a configuration management service might take a week, the three adjustable constants I’ve modified over the past three months required only a mere