The Three-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the tech industry, it is commonly advised to “do things that don’t scale,” a mantra made famous by Paul Graham. Yet, discussions on how to effectively incorporate this principle into software development are scarce. Over the past eight months, while building my AI podcast platform, I’ve devised a simple strategy that I call the “Three-Month Rule.” This framework allows me to test unscalable tactics for a limited, three-month period. After this time frame, the tactic must either demonstrate its worth and be properly integrated or be discarded.
As engineers, we often fall into the trap of designing scalable solutions from the outset—think elaborate architectures like microservices and distributed systems intended to serve vast user bases. However, this approach often leads startups to chase after scalability prematurely, causing delays and inefficiencies in addressing current user needs. My three-month rule emphasizes producing straightforward, albeit “imperfect,” code that allows me to gain insight into what users genuinely require.
Current Infrastructure Hacks: Why They Work
Let me share some of my unconventional methods that have proven not only functional but also educational:
1. Consolidated Resources on a Single Virtual Machine
Everything from the database to the web server, background jobs, and more resides on one $40/month virtual machine, with no redundancy and manual backups to my local system.
Why is this a strategic choice? In the last two months, I’ve gained more understanding of my genuine resource requirements than I would have through extensive documentation or theoretical estimates. I’ve discovered that my “AI-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes setup I nearly implemented would likely have involved managing resources I didn’t even need. When failures occur (and they have), I receive invaluable insights about what specifically went wrong, often surprising me.
2. Simplified Configuration Management
In my code, you’ll find hardcoded configurations such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem primitive—lacking config files or environment variables—this approach allows me to quickly locate and modify values. Each adjustment is tracked in version control and warrants a code review, albeit a personal one.
Why reinvent the wheel? Building a configuration service would take considerable time, while
