Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Coding
In the realm of startup development, one piece of advice stands out: “Do things that don’t scale,” a mantra famously voiced by Paul Graham. However, the question remains — how can we effectively apply this principle in the programming world? After eight months of building my AI podcast platform, I’ve developed a straightforward guideline that I call the 3-Month Rule. This framework gives every unscalable solution a lifespan of three months. At the end of this period, if a solution proves its worth, it undergoes a proper overhaul; if not, it’s time for it to go.
The startup Reality Check
As engineers, we often feel compelled to create scalable solutions from the outset. Our training emphasizes sophisticated paradigms—think microservices and distributed systems—to handle vast user bases. Yet, this mindset can lead to costly delays in a startup environment. In many cases, optimizing your code for users who may never arrive is an exercise in futility. By adhering to my 3-Month Rule, I’m able to write straightforward, faster-tracked code that actually gets deployed, allowing me to understand user requirements first-hand.
Key Unscalable Hacks: Practical Insights
1. Everything on One Server
I run my entire operation — database, web server, background services, and Redis — on a single $40/month virtual machine (VM) without redundancy and conduct manual backups to my local machine. This setup might seem reckless, but within just two months, I’ve gained invaluable insights into my resource requirements. My so-called “AI-heavy” platform peaks at just 4GB of RAM. If I had implemented a more complex architecture like Kubernetes, I would have ended up managing resources that aren’t even necessary.
Each time the system crashes, I gather real-time data on what fails — and, surprisingly, it’s seldom what I anticipated.
2. Simplified Configuration Management
Instead of relying on config files or environment variables, I use hardcoded constants throughout my code:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While some may see this as a limitation, it has its benefits. I can quickly search through my codebase for any configuration values, and all price changes are recorded in Git history. The effort to
