Embracing the 3-Month Rule: A Framework for Non-Scalable Innovation in Tech Development
In the world of startups, the mantra “Do things that don’t scale” often echoes in the halls of successful entrepreneurship, famously advocated by Paul Graham. However, the question remains: how do we effectively apply this concept in the realm of coding and technology development? Over the past eight months of building my AI podcast platform, I’ve adopted a straightforward yet impactful approach I call the “3-Month Rule.” This rule allows every temporary, unscalable hack a lifespan of three months to prove its worth. After this period, we can assess whether it’s time for enhancement or termination.
In engineering, we are typically trained to focus on scalable solutions from the outset, utilizing design patterns and robust architectures that cater to millions of users. While this structured thinking is useful in larger corporations, it can lead to misplaced efforts in startups, where scaling often turns into expensive delays. By adhering to the 3-Month Rule, I’m compelled to develop simple and sometimes “messy” code that can be deployed quickly, allowing me to better understand genuine user needs.
Current Infrastructure Hacks that Propel Learning
1. Consolidated Operations on a Single VM
All elements of my application—database, web server, background jobs, and caching—are hosted on a modest $40/month virtual machine. This setup, while seemingly lacking in redundancy, has provided invaluable insights into my actual resource requirements. Within just two months, I discovered that my “AI-heavy” platform operates efficiently with merely 4GB of RAM. Instead of creating a complex Kubernetes environment that would manage idle resources, I’ve learned firsthand what breaks during a crash, which is often not what I anticipated.
2. Directly Hardcoded Configuration
Instead of utilizing separate configuration files or environment variables, I’ve opted for directly embedded constants within my code. This approach enables me to quickly search through my entire codebase to track any configuration changes. In three months, I’ve made only three adjustments to these constants, resulting in a far more efficient operation than developing a comprehensive configuration management system.
3. Using SQLite for Production
Contrary to conventional wisdom, I chose to implement SQLite for my multi-user application. With a mere 47MB database, it handles up to 50 concurrent users seamlessly. This choice revealed that my application primarily consists of 95% read operations and only 5% writes,
