Embracing the 3-Month Rule: My Framework for Effectively Implementing Non-Scalable Solutions
In the world of startup development, renowned entrepreneur Paul Graham famously advises, “Do things that don’t scale.” While this notion is widely accepted, the practical implementation of this advice in software development often goes unexamined. After dedicating eight months to building my AI podcast platform, I’ve established an innovative framework: every non-scalable approach is given a three-month trial period. Following this timeframe, I evaluate whether the strategy has demonstrated its worth, leading to a robust solution, or if it should be discarded.
As engineers, we are often conditioned to prioritize scalability from the outset, crafting sophisticated design patterns, microservices, and distributed systems capable of supporting vast user bases. While this is undoubtedly essential for larger organizations, it can be counterproductive in a startup environment. Focusing on scalability too soon frequently amounts to costly delays and unnecessary complexity. My three-month rule compels me to write straightforward, less elegant code that actually gets deployed, providing valuable insights into user needs.
Current Infrastructure Hacks: Simple Choices Leading to Smart Insights
1. Consolidating Everything on a Single VM
I’ve opted to run my database, web server, background jobs, and Redis on a single virtual machine costing just $40 per month. This choice sacrifices redundancy and relies on manual backups. The brilliance of this approach lies in the clarity it provides regarding my actual resource usage. Within a mere two months, I’ve learned that my platform reaches a peak of just 4GB of RAM, rendering the complex Kubernetes setup I initially contemplated unnecessary.
When technical issues arise—it’s happened twice so far—I gain precise data about the failures, often revealing unexpected insights.
2. Using Hardcoded Configuration
For my project, all configuration settings are hardcoded, with no config files or environment variables—just straightforward constants distributed throughout the codebase. While this may seem impractical, it allows for rapid searches and easy tracking of changes in Git history. Over three months, I’ve modified configuration settings only three times, making this method far more efficient than developing a dedicated configuration service.
3. Employing SQLite in Production
Despite running a multi-user application, I chose SQLite as my database solution, which efficiently manages a mere 47MB and accommodates 50 concurrent users without issue. This decision has proven enlightening, revealing that my data access patterns consist of 95% reads and only 5% writes. Had I
