The 3-Month Rule: A Practical Approach to Non-Scalable Development

In the startup ecosystem, embracing Paul Graham’s mantra to “do things that don’t scale” can be both liberating and challenging. However, when it comes to coding, the execution of this principle is rarely discussed. Through my experience of building an AI podcast platform over the last eight months, I have established a unique framework: each unscalable approach is allotted precisely three months. At the end of this period, it must either demonstrate its value and be re-engineered, or it will be discarded.

As developers, we are often conditioned to prioritize scalability from day one, focusing on sophisticated design patterns, microservices, and sprawling architectures optimized for vast user bases. However, in a startup environment, prioritizing scalability can lead to expensive procrastination—spending effort on solutions for users that don’t even exist yet. My three-month rule enables me to create straightforward, albeit “bad,” code that can actually be deployed. This method ensures that I learn what users truly need.

Current Tactics: Unconventional Methods for Real Insights

1. Unified Virtual Machine Deployment

I am currently operating everything—my database, web server, background tasks, and even Redis—on a single virtual machine that costs just $40 per month. This setup is free from redundancy, with manual backups saved locally.

The rationale behind this approach is simple: in just two months, I have acquired instant insight into my resource requirements, far surpassing what any detailed capacity document could reveal. My AI-driven platform only peaks at 4GB RAM usage, indicating that the elaborate Kubernetes configuration I nearly implemented would have been wasted on managing inactive containers. When my system does crash (which has happened twice), I receive practical data about the actual failure points—often not aligning with my expectations.

2. Hardcoded Configuration Values

For configuration, I rely on hardcoded constants scattered throughout my codebase. No separate configuration files or environment variables—just straightforward settings, such as:

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

Changing a single value necessitates a redeployment, making each change traceable through git history. This straightforward method allows me to quickly search for configurations without the complications of a dedicated config service, saving significant engineering time for the rare occasions when