Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions
In the world of startups, practicality often trumps perfection. Many entrepreneurs have resonated with Paul Graham’s famous mantra: “Do things that don’t scale.” However, the real challenge lies in translating this advice into actionable steps within the realm of software development. After eight months of curating my AI podcast platform, I’ve crafted a straightforward methodology: any unscalable workaround gets a three-month trial period. At the end of this timeframe, it either earns its place with a robust implementation or is phased out.
The Context: Rethinking “Scalability” in Startups
As developers, we often have our minds set on building scalable solutions from the outset. The allure of perfect architecture—microservices, distributed systems, and sophisticated design patterns—beckons us, but this perspective can be a pitfall, especially in a startup environment. Focusing on scalability prematurely can turn into costly procrastination, as it directs our efforts toward users who may not yet exist and problems that haven’t arisen.
To combat this, my three-month rule encourages me to build straightforward, albeit less-than-ideal, code that actually gets deployed. This hands-on approach reveals vital insights into user needs.
A Peek into My Current Hacks and Their Surprising Benefits
1. One VM to Rule Them All
I’m currently running my entire infrastructure—a web server, database, background jobs, and caching—all from a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has drastically improved my understanding of resource needs. Within just two months, I learned that my platform’s peak memory usage is only 4GB. Instead of wasting resources on an intricate Kubernetes setup, I now focus on optimizing real issues based on genuine data from system crashes.
2. Simplistic Configuration Management
I’ve opted for hardcoded values in my codebase rather than using configuration files or environment variables. For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This choice allows for quick adjustments and easy tracking of changes through Git. Over three months, I’ve changed configuration settings only three times, making a swift redeployment more efficient than constructing a full-fledged configuration service.
