Embracing the 3-Month Rule: A Streamlined Approach to Development
In the startup world, where rapid experimentation and adaptability reign supreme, Paul Graham’s mantra of “do things that don’t scale” has become a staple for entrepreneurs. However, translating that philosophy into the technical realm is rarely straightforward. After dedicating eight months to the development of my AI podcast platform, I’ve established a practical framework known as the “3-Month Rule.” This principle allows unscalable hacks to flourish for three months; if they validate their worth by that time, they either evolve into robust solutions or are discarded.
As engineers, our instinct is to construct scalable infrastructures from the outset. Concepts like microservices, distributed systems, and elaborate architectures are alluring—but they often represent a mindset more suited to larger enterprises. In the startup environment, focusing on scalable solutions can become a costly form of procrastination, optimizing for users who may not even exist yet. My approach compels me to write straightforward, even “subpar” code that delivers results, revealing the genuine needs of my users.
Current Infrastructure Hacks: Strategic Simplicity
1. Consolidated Resource Usage
I operate my entire platform on a single virtual machine (VM)—databases, web servers, and background jobs reside together on a modest $40/month service. While this approach lacks redundancy and relies on manual backups, it has provided invaluable insights into my actual resource requirements. In just two months, I’ve identified that my “AI-focused” platform only requires 4GB of RAM during peak activity. The complex Kubernetes setup I nearly pursued would likely have ended up managing dormant containers.
When the system has gone down (twice), the feedback was eye-opening, revealing unexpected failure points.
2. Hardcoded Configuration Values
I’ve opted for direct constants in my codebase rather than environment variables or configuration files:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method might appear rudimentary, requiring redeployment for any changes, but the efficiency pays off. I can quickly search for any configurations throughout the entire codebase. With only three changes made in the past three months, the time saved in deployment far outweighs the seven days it would take to create a dedicated configuration service.
3. SQLite on the Edge
Contrary to conventional wisdom, I
