Navigating Growth: Embracing the 3-Month Rule for Unscalable Solutions
In the startup world, we often hear the sage advice from Paul Graham: “Do things that don’t scale.” While this concept is well-known, the practical implementation, especially in terms of coding, tends to remain uncharted territory.
Over the past eight months, my journey in developing an AI podcast platform has led me to adopt a simple yet effective framework: any unscalable solution is permitted to live for only three months. After this period, it either demonstrates its worth and is refined into a robust system or is discarded.
As engineers, we are naturally inclined to construct scalable solutions right from the outset, dreaming in terms of design patterns, microservices, and distributed systems that can accommodate millions of users. However, this is typically the mindset of larger organizations. In the startup environment, focusing solely on scalability often leads to expensive delays, as you might be optimizing for future users who aren’t yet on the horizon and addressing problems that may never materialize. My three-month approach compels me to create straightforward, albeit “messy,” code that is deployable and, crucially, informs me about the real needs of my users.
My Approach: Infrastructure Hacks That Actually Work
1. Single VM Architecture
Everything — from the database to the web server, background jobs, and caching — operates on a single $40/month virtual machine. This setup is devoid of redundancy and relies on manual backups to my local system.
Why It’s Genius: In just two months, I’ve gained a clearer understanding of my resource needs than any capacity planning document could provide. I learned that my “AI-intensive” platform requires only 4GB of RAM at peak usage. The sophisticated Kubernetes infrastructure I considered implementing would have wasted resources managing idle containers. Every crash has revealed genuine insight into failure points — and they’re rarely what I anticipated.
2. Hardcoded Configuration
From defining pricing tiers to setting user limits, all my configuration values are hardcoded as constants. Modifying these parameters necessitates a redeployment.
The Advantage: This method allows me to quickly search my entire codebase for any configuration value, and every change is tracked in version control. In the past three months, I have adjusted these values just three times, which meant 15 minutes of redeployment compared to a week’s worth of engineering effort.
