Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions
In the world of startups, the mantra “Do things that don’t scale,” popularized by Paul Graham, is well-known but somewhat elusive when it comes to practical implementation, especially in coding. After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework that I’ve dubbed the “3-Month Rule.” This method mandates that any unscalable hack I implement has a trial period of three months. Following this duration, it either proves its worth and is developed further or is discarded.
The Scalable Illusion in startup Development
As software engineers, we’re often drilled on the importance of scalable solutions from the outset. We become enamored with design patterns, microservices, and distributed architectures that are intended to cater to millions of users. However, this approach may not suit the startup landscape. In fact, focusing on scalable code too early can lead to costly procrastination—an exercise in optimizing for users that may never materialize. By adhering to my 3-month rule, I’ve been compelled to write simpler, more direct code that gets deployed and reveals genuine user needs.
Highlighting My Current Infrastructure Hacks
1. Consolidated Operations on a Single VM
At the heart of my setup lies a single $40/month virtual machine hosting my database, web server, background jobs, and Redis. This minimalist approach comes with zero redundancy and manual backups to my local storage. You might question the wisdom of this setup, but it has granted me invaluable insights into my actual resource requirements. Within two months, I discovered my platform peaks at just 4GB of RAM. Instead of juggling an intricate Kubernetes architecture filled with empty containers, I’ve amassed real-world crash data that highlights unexpected breaking points.
2. Simplified Hardcoded Configurations
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I keep configurations simple and scattered throughout my code as hardcoded constants. This method reduces the overhead of separate config files and allows me to search my codebase quickly for any value. Over the past three months, I’ve changed these constants only three times—a trivial cost in redeployment compared to the extensive engineering effort required to build a full-fledged configuration service.
