Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Software Development

In the world of software development, the advice from renowned investor Paul Graham rings true: “Do things that don’t scale.” Yet, the challenge remains: how do you implement this philosophy effectively, particularly in coding?

After spending eight months developing my AI podcast platform, I have devised a practical strategy that I call the 3-Month Rule. This framework operates on a simple principle: every hack that doesn’t scale receives a three-month trial period. At the end of this timeframe, we evaluate if it is worthy of a proper implementation or if it is time to let it go.

As engineers, we often find ourselves pressured to create scalable solutions right from the outset. We dive into design patterns, microservices, and distributed systems, all aimed at handling potentially millions of users. However, in a startup environment, pursuing scalable solutions too early often leads to needless complexity and wasted resources. The 3-Month Rule prompts me to produce straightforward, albeit unpolished, code that is functional and, more importantly, educational. It allows me to understand the real needs of my users.

My Current Infrastructure Strategies and Their Unexpected Wisdom

1. All Services on a Single Virtual Machine

I’ve consolidated my database, web server, background jobs, and Redis onto a single $40/month virtual machine. Yes, there’s no redundancy, and I handle backups manually.

What I’ve learned in just two months has been invaluable. Rather than pouring over capacity planning documents, I have firsthand insights into my resource demands. My platform, heavily reliant on AI, only peaks at 4GB of RAM. The Kubernetes architecture I considered would have simply wasted resources on idle containers. Each crash (yes, there have been two) has provided real-world data on where my system struggles, and interestingly, it’s rarely where I expected.

2. Hardcoded Configuration Values

Instead of opting for configuration files or environment variables, I utilize hardcoded constants for settings like pricing tiers and user limits:

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

The beauty of this approach? I can quickly search my entire codebase for any configuration value. Changes are easy to track through git, with each update going through a code review—