Embracing the Three-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech

In the tech world, there’s a well-known edict from Paul Graham: “Do things that don’t scale.” Yet, there’s often a lack of discussion on how to apply this wisdom effectively during the coding process.

While developing my AI podcast platform over the past eight months, I’ve crafted a straightforward yet highly effective framework: each unscalable solution is granted a lifespan of three months. After this period, it either demonstrates its worth and is reintegrated into a more robust system, or it’s discarded.

This methodology stems from a common scenario in startups, where the tendency to focus on scalable systems from the outset can lead to costly delays and unnecessary complexity. As engineers, we’re conditioned to architect solutions that can accommodate millions of users, but this mindset can sometimes hinder the agile and iterative learning necessary for early-stage platforms.

Why the Three-Month Rule Works:

1. Concrete Insights on Resource Needs:
   I run my entire operation on a single $40/month virtual machine that accommodates the database, web server, background jobs, and Redis. While this setup lacks redundancy and entails manual backups to my local machine, it has provided invaluable insights into my genuine resource requirements. In just two months, I identified that my platform only peaks at 4GB of RAM, revealing that the elaborate infrastructure I initially planned would have been wasted effort.

2. Simplified Configuration Management:
   Instead of elaborate configuration files or environment variables, I’ve opted for hardcoded constants throughout my code. This strategy allows quick searches across my entire codebase and effortless tracking of changes through version control. The time saved by avoiding a complex configuration service—especially given that most variables have only changed three times in three months—justifies this approach.

3. Utilizing SQLite in a Multi-User Environment:
   Surprisingly, I employ SQLite for my multi-user application, which has proven to be efficient with its 47MB database size. My investigation revealed that my system is primarily read-heavy, which aligns perfectly with SQLite’s strengths. Had I opted for a more sophisticated database solution initially, I would have wasted efforts on optimizations that weren’t necessary yet.

4. Streamlined Deployment Process:
   My deployment process is incredibly straightforward, requiring only a single command to push updates to production. This simplicity has the dual advantage of promoting intentional, focused changes while allowing me