Embracing the 3-Month Rule: A Practical Approach to Unscalable Coding

In the startup world, we often hear the advice from tech luminary Paul Graham: “Do things that don’t scale.” Yet, the challenge lies not just in understanding this mantra but also in knowing how to strategically apply it in code development. After spending eight months building my AI podcast platform, I’ve devised a framework that focuses on a strict three-month timeline for any unscalable methods. At the end of this period, it either demonstrates its worth and gets improved for scaling or gets scrapped altogether.

As engineers, we’re conditioned to architect solutions for scalability from the outset. We often get caught up in the allure of design patterns and robust microservice architecture that can accommodate millions of users. While this approach has its merits—especially for larger companies—it can become a pitfall for startups. Tackling optimization for potential future users may lead us down a path of costly delays to address hypothetical challenges. My three-month rule encourages me to prioritize writing simple, straightforward code that delivers real value and enhances my understanding of users’ needs.

Current Infrastructure and My Unorthodox Approach

1. Unified Virtual Machine

Every aspect of my operation, from the database to web services, resides on a single virtual machine costing merely $40 a month. There’s no redundancy and backups are manual.

Why is this a smart choice? Within just two months, I’ve gained a clearer picture of my actual resource requirements than any planning document could provide. For instance, my platform’s peak consumption is only 4GB of RAM. The complex Kubernetes environment I nearly created would have resulted in idle resource management.

When issues do arise (and they have), I receive invaluable feedback about failure points, revealing insights that are often unexpected.

2. Hardcoded Configuration Values

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

Instead of relying on configuration files or environment variables, I’ve opted for hardcoded constants. This decision mandates a deployment for any change, but it yields several advantages. For one, I can rapidly search my entire codebase for any configuration value. Each adjustment is documented in the version history, and even self-reviewed.

Creating a separate configuration service would have consumed a week of development time. Instead, I’ve altered these values just three times in