Embracing Imperfection: The 3-Month Rule for Scalable Development

In the world of startup development, a common piece of wisdom often shared by entrepreneurs and innovators is Paul Graham’s advice to “do things that don’t scale.” While this mantra is widely recognized, little is said about the practical steps one can take to apply this philosophy, especially when it comes to coding.

After 8 months of developing my AI podcast platform, I’ve established an approach I like to call the “3-Month Rule.” Under this framework, any unscalable hack implemented in the development over a period of three months faces a critical evaluation: it either has to demonstrate its value and evolve into a more robust solution, or it gets discarded.

Why the 3-Month Rule Matters

As engineers, our instincts often guide us to prioritize scalability from the outset. We dive into intricate architecture designs, focusing on microservices, distributed systems, and high-level abstractions meant to cater to millions of users. However, in the context of a startup, chasing scalability too early is frequently a costly form of procrastination. This often leads to the construction of elaborate solutions for problems that haven’t yet materialized. My three-month time frame encourages me to develop straightforward, even if flawed, code that yields real insights about what users genuinely require.

My Pragmatic Infrastructure Hacks

1. One Virtual Machine for Everything

I run my entire stack—database, web server, background jobs, and caching on a single $40/month virtual machine. There’s no redundancy and I manually back up data to my local system.

You might think this is a risky move, but it has provided invaluable insights into my actual resource needs over the past two months. Surprisingly, my resource peaks are manageable; my “AI-heavy” platform only requires about 4GB of RAM. What initially seemed like a necessary Kubernetes setup would have resulted in managing idle containers. When the system crashes (which has happened twice), I gain authentic data about failure points—insights I wouldn’t have anticipated.

2. Hardcoded Configurations

Instead of going for external configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase. Making a change requires a simple redeployment.

This approach allows me to easily search and track configuration changes through Git history. Since implementing this, I’ve made only three configuration updates, saving me significant time compared to creating a dedicated configuration management service.