Embracing the 3-Month Rule: A Practical Guide to Non-Scalable Solutions in Tech Development

When it comes to the world of software development, you might have encountered the well-known phrase by Paul Graham: “Do things that don’t scale.” While this advice is commonly referenced, the practical application of it, especially in coding, often remains underexplored.

Throughout my eight-month journey of building an AI podcast platform, I’ve devised a practical approach I like to call the “3-Month Rule.” This simple yet effective framework mandates that every unscalable workaround is granted a mere three months to demonstrate its worth. After this period, a decision must be made: either elevate it to a robust solution or let it fade away.

Contextualizing the 3-Month Rule

As technologists, we often lean toward constructing scalable solutions from the start. We think in terms of design patterns, microservices, and intricate distributed systems—all aimed at accommodating thousands (if not millions) of users. This mindset serves larger organizations well but may inhibit startups that need to adapt quickly.

In my experience, pursuing scalability often leads us to procrastinate on the essential tasks that would propel our projects forward. Rather than focusing on potential future users, my 3-Month Rule encourages me to embrace simpler, more immediate coding methodologies that deliver tangible results and insights about user needs.

My Simplistic Infrastructure Hacks: Practical Insights

1. Operating on a Single Virtual Machine

For merely $40 a month, my entire tech stack runs on a single virtual machine, encompassing everything from the database to background jobs. While this setup lacks redundancy and relies on manual backups, it has been enlightening. I’ve learned more about my resource usage in two months than through any elaborate capacity-planning session. The system’s crashes, which have occurred twice, provided unexpected data about vulnerabilities I never anticipated.

2. Hardcoded Configurations

Rather than utilizing configuration files or environment variables scattered throughout my system, I opted for hardcoded constants. This design decision means that modifying a configuration results in a code redeployment, but it grants remarkable benefits—specifically, the ability to quickly search through my codebase for any configuration value and track changes via version control. The hours saved developing an extensive configuration management system for an infrequent adjustment are significant.

3. Implementing SQLite in Production

Utilizing SQLite for a multi-user application may seem unconventional, but its performance with a