Embracing the 3-Month Rule: A Technical Approach to Non-Scalable Solutions
In the tech community, the wisdom of Paul Graham’s mantra, “Do things that don’t scale,” is widely recognized. However, the conversation often lacks depth regarding practical implementation, particularly in software development.
After eight months of building my AI podcast platform, I’ve crafted a straightforward framework that I refer to as the “3-Month Rule.” This approach stipulates that any unscalable solution is granted three months to prove its worth. If it doesn’t demonstrate sufficient value by that time, it’s up for elimination.
As engineers, our training typically focuses on developing scalable solutions from the outset. We become enamored with architectural designs like microservices and distributed systems, all tailored to support vast user bases. However, such thinking can be overly ambitious for startups, where scalable code often equates to costly procrastination. We focus on hypothetical users and problems that may never arise, delaying the development of more immediate needs. The 3-Month Rule encourages me to create straightforward, “imperfect” code that actually gets implemented and reveals genuine user requirements.
How My Current Technical Solutions Are Actually Smart
1. Consolidated Infrastructure on One VM
I currently run my database, web server, background jobs, and Redis on a single virtual machine that costs me $40 per month. While this setup lacks redundancy and relies on manual backups to my local system, it has provided invaluable insights into my resource needs. Within two months of operation, I’ve determined that my AI-driven platform peaks at 4GB of RAM, a revelation that could have been lost amid the complexities of orchestrating a Kubernetes environment. When crashes do occur (and they have, twice), I gather real-time data that highlights unexpected failure points.
2. Simplified Hardcoded Configurations
By hardcoding configurations directly in the codebase—like pricing tiers and user limits—I’ve made updates straightforward. Changing any parameter requires a redeployment, which is minimal compared to the time investment of developing a separate configuration service that I might use sparingly. I can search my entire codebase for any specific setting within seconds, and this simplicity has kept me agile.
3. Utilizing SQLite for Production
While unconventional, running SQLite for my multi-user web application has been a successful choice. The entire database weighs in at just 47MB and efficiently supports 50 concurrent users. This decision stemmed from my investigation of access patterns
