Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions
In the world of start-up culture, the adage from Paul Graham—”Do things that don’t scale”—is frequently cited, yet seldom translated into actionable strategies for developers. Over the past eight months, while building my AI podcast platform, I’ve found a unique approach to applying this wisdom: enter the 3-month rule. This framework suggests that every temporary, unscalable hack gets a trial period of three months. After that, we make a decision: enhance its functionality or eliminate it.
As engineers, we often get caught up in the allure of designing scalable solutions from the outset. We envision architecting systems with elegant patterns like microservices and distributed systems, all tailored to cater to vast numbers of users. However, this expansive outlook usually belongs to larger enterprises. In a startup environment, pursuing scalable architecture can sometimes lead to costly procrastination, focused on hypothetical future users or problems.
My 3-month rule compels me to create straightforward, sometimes inelegant code. This allows me to publish functional products quickly and learn first-hand about my users’ real needs.
Current Infrastructure Hacks: A Smart Approach to Learning
1. Consolidating Everything on a Single VM
At the moment, my database, web server, background jobs, and caching run on a single virtual machine costing just $40 per month. While this approach lacks redundancy and relies heavily on manual backups, it has produced invaluable insights. Within just two months, I’ve gained a better understanding of my resource requirements than any software capacity plan could have offered. I realized that my “AI-focused” platform only lifts its own weight at 4GB of RAM, thus preventing me from falling for the trap of orchestrating unnecessary Kubernetes clusters.
When my system crashes—which has happened on two occasions—I acquire genuine feedback about what fails. Unsurprisingly, it’s seldom what I’d anticipated.
2. Relying on Hardcoded Configurations
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no separate configuration files or environment variables here. Instead, I’ve opted for constants throughout my codebase, meaning that every time I need to adjust something, it triggers a straightforward redeployment.
The beauty of this method? I can quickly search
