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

In the world of startups, the mantra of “do things that don’t scale” often resonates, yet the implementation of this philosophy in the realm of coding remains an underexplored territory. Having spent the past eight months constructing my AI podcast platform, I have developed a straightforward methodology—introducing the 3-Month Rule. Each unscalable approach is permitted a lifespan of three months, after which it must either validate its worthiness for further development or face removal.

The Dichotomy of Scaling in startup Culture

As engineers, we are often conditioned to design for scalability from the outset: intricate architectures using microservices, distributed systems, and design patterns that can handle massive user bases are the norm. However, for a burgeoning startup, prioritizing scalable solutions can feel like an exercise in procrastination. In many cases, we find ourselves attempting to optimize for hypothetical users who may never materialize and tackling problems that are not yet a concern.

My three-month strategy compels me to create straightforward, sometimes “imperfect,” code that moves swiftly to deployment, enabling me to glean insights about user needs effectively.

Current Infrastructure Hacks and Their Wisdom

1. Unified Infrastructure on a Single VM

I operate my entire platform—including the database, web server, background jobs, and caching—on a single $40/month virtual machine. There’s no redundancy, and backups are manually saved on my local device.

Why is this practical, you may wonder? It has allowed me to assess my actual requirements far more effectively than any theoretical capacity planning document could. With my platform peaking at just 4GB RAM, I realized that the sophisticated Kubernetes infrastructure I nearly implemented would have been wastefully managing idle containers. Moreover, when the server does crash (as it has twice), I gather concrete data about the real causes, often surprising revelations that differ from my initial expectations.

2. Simplified Configuration Management

Instead of relying on configuration files or environment variables, I keep constants directly within my code:

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

While this approach might appear archaic, the speed of searching through the codebase for any configuration value is a significant advantage. Tracking price alterations through git history proves beneficial, and though