Embracing the 3-Month Rule: A Tactical Approach to Development for Startups
In the ever-evolving landscape of tech entrepreneurship, one piece of wisdom often echoed by thought leaders like Paul Graham is to “do things that don’t scale.” However, the challenge lies in translating this mindset into actionable strategies within the realm of coding and software development.
Over the past eight months, while building my AI podcast platform, I’ve honed a distinct method called the 3-Month Rule. This framework dictates that every unscalable approach I adopt is given a trial period of three months. Within this timeframe, each hack must either demonstrate its effectiveness, warranting a more robust implementation, or be phased out.
As engineers, we tend to gravitate toward creating scalable solutions from the outset, prioritizing intricate architectures, microservices, and distributed systems that are designed to handle vast user bases. However, this mindset is often more suited to larger companies, making it counterproductive in the startup ecosystem. In fact, prematurely optimizing for future users can lead to wasted resources, addressing potential challenges that might never arise.
The 3-Month Rule encourages me to write simple, straightforward code that allows me to gather essential insights about my users’ needs in real-time. Here are some of the pragmatic infrastructure decisions I’ve made that effectively illustrate this principle:
1. Consolidated Operations on a Single Virtual Machine
My entire tech stack — including the database, web server, background jobs, and caching — operates on a single $40 per month virtual machine, with no redundancy and manual backup procedures.
This approach has been invaluable; in just a couple of months, I’ve gained a clearer understanding of my actual resource requirements than any planning document could provide. For instance, my “AI-heavy” platform peaks at only 4GB RAM. The sophisticated Kubernetes infrastructure I nearly implemented would have meant managing empty containers instead of real usage. When the system has crashed (twice), I learn precisely what went wrong — and it’s rarely what I anticipated.
2. Simplified Configuration Management
My configuration is hardcoded into the application, where values like prices and user limits are directly embedded within the code itself.
While it might seem inefficient to redeploy for any adjustments, the clarity it provides is worth it. I can search through the entire codebase in seconds for any configuration value, and every change is carefully documented in my Git history. Instead of spending a week building a complex configuration service,
