The 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the world of technology startups, the advice from industry leaders often resonates: “Embrace what doesn’t scale.” However, translating this philosophy into concrete coding practices remains relatively unexplored. After eight months of developing my AI podcast platform, I’ve devised a practical framework that I call the 3-Month Rule. This concept allows me to push “unscalable hacks” to their limits for a set period—three months. If these hacks prove their worth, they are refined and integrated into the core system; if not, they are retired.
Rethinking Scalability
As engineers, we typically gravitate toward scalable solutions from the outset, envisioning architecture capable of supporting millions of users. While this is essential for larger enterprises, it can often lead to costly delays in startups. For burgeoning projects, focusing on scalability early can divert attention from immediate user needs, compounding unnecessary complexities. My rule encourages writing straightforward, if imperfect, code that delivers real-world insights into user behavior and requirements.
My Current Tech Hacks: Strategic Choices Behind Simplicity
1. Single VM Infrastructure
I’ve opted to run my entire platform—from the database to the web server—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided invaluable lessons about my actual resource utilization. In just two months, I discovered that the “AI-heavy” system peaks at 4GB of RAM. My initial impulse to implement an elaborate Kubernetes configuration would have resulted in managing empty resources instead of delivering value. When failures occur, I gather practical data that informs future improvements.
2. Directly Hardcoded Configuration Values
Instead of utilizing complex configuration files or environment variables, I’ve hardcoded crucial values directly into my code. Changing a configuration means redeploying the application—a quick process that I can track through Git’s history. This method may appear rudimentary, but it allows for rapid access and adjustments. Over three months, I’ve only needed to modify these values three times; the efficiency gained here far outweighs the time investment of building a dedicated configuration service.
3. Using SQLite in Production
I’ve chosen to run SQLite for my multi-user web application, which, though unconventional, has proven effective. The database is only 47MB and efficiently manages up to 50 concurrent users without compromising performance. This choice taught me valuable lessons
