Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Development

In the realm of startup culture, the well-known advice from Paul Graham—“Do things that don’t scale”—often resonates profoundly, yet the practical implementation of this mantra in coding remains lesser discussed. After eight months of developing my AI podcast platform, I’ve adopted a straightforward yet effective framework I call the “3-Month Rule.” This approach allows unscalable hacks a limited lifespan. At the end of three months, if these hacks demonstrate their value, they receive the resources to be fully developed; otherwise, they are discarded.

As engineers, we’re frequently conditioned to focus on scalable solutions right from the outset. Whether it’s through establishing design patterns, microservices, or distributed systems, we are trained to construct intricate architectures meant to support millions of users. However, this mindset is more suited for larger enterprises rather than the nimble landscape of startups.

In the startup world, preemptively developing scalable code can often become a costly form of procrastination. It leads us to solve problems for imaginary users, while altruistic work towards a scalable architecture can obscure the immediate needs of our actual customer base. My 3-Month Rule compels me to write straightforward, sometimes “messy,” code that actually gets deployed, which in turn, provides invaluable insights into what users truly require.

Current Infrastructure Strategies: The Smart Hacks

1. Consolidation on a Single Virtual Machine

Currently, I operate all my components—database, web server, background jobs, and Redis—on a single $40/month virtual machine with no redundancy and manual backups to my personal device.

You might think this approach is reckless, but it has its brilliance. In a mere two months, I’ve gained more knowledge about my resource requirements than any typical capacity planning document could provide. It turns out my “AI-heavy” platform rarely exceeds 4GB of RAM. The overly complex Kubernetes setup I nearly implemented would have merely managed idle containers.

When the system has failed (which has occurred twice), it has benefited my understanding. The points of failure were never what I initially anticipated.

2. Simplified Hardcoded Configuration

I’ve opted for hardcoded configurations throughout my codebase, such as:

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