The 3-Month Rule: A Pragmatic Approach to Development for Startups
When it comes to building a successful product, everyone has heard the advice from tech visionary Paul Graham: “Do things that don’t scale.” However, translating this wisdom into practical coding strategies is often overlooked. After eight months of developing my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule.” In essence, any unscalable approach gets a trial period of three months. At the end of this timeframe, it either validates its worth and is transitioned to a robust solution or is simply discarded.
The Challenge of Scalable Thinking
As engineers, we are trained to prioritize scalability from the outset. Our minds often leap to intricate design patterns, microservices, and distributed systems capable of managing millions of users. While these architectures are ideal for established companies, they can be a hindrance for startups.
In the startup ecosystem, crafting scalable solutions from the beginning can often turn into a costly form of procrastination. It involves preparing for users who may not even be there yet and addressing challenges that may never arise. My three-month rule pushes me to embrace simpler, albeit less refined, coding practices that yield functional products. This, in turn, helps me identify the genuine needs of my users.
Current Infrastructure Insights
1. Single VM Architecture
My entire setup runs on one $40/month virtual machine, hosting the database, web server, background jobs, and Redis without redundancy or automatic backups—just manual ones to my local drive.
Why is this a savvy choice? Within just two months, I’ve gained invaluable insights into my actual resource demands. It turns out my “AI-heavy” platform rarely taps beyond 4GB of RAM. The elaborate Kubernetes configuration I almost pursued would have only involved managing idle containers. When my system crashes—yes, it has happened twice—I’m provided with tangible data on what truly fails. Spoiler alert: it’s never what I anticipated.
2. Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I don’t utilize configuration files or environment variables. Instead, I’ve opted for constants throughout the codebase, meaning changes necessitate a redeployment.
This choice has a hidden advantage: I can quickly search my entire codebase for a config value in
